InteroceptionScale (study 2) - Data Analysis

Data Preparation

library(tidyverse)
library(easystats)
library(patchwork)
library(ggside)
library(EGAnet)
library(tidygraph)
library(ggraph)

set.seed(42)

Sys.setenv(CHROMOTE_CHROME = "C:\\Program Files (x86)\\Microsoft\\Edge\\Application\\msedge.exe")

df <- read.csv("../data/data_participants.csv") |> 
  rename(MINT_Urin_1 = MINT_Deficit_Urin_1,
         MINT_Urin_2 = MINT_Deficit_Urin_2,
         MINT_Urin_3 = MINT_Deficit_Urin_3,
         MINT_CaCo_1 = MINT_Deficit_CaCo_4,
         MINT_CaCo_2 = MINT_Deficit_CaCo_5,
         MINT_CaCo_3 = MINT_Deficit_CaCo_6,
         MINT_CaNo_1 = MINT_Deficit_CaNo_7,
         MINT_CaNo_2 = MINT_Deficit_CaNo_8,
         MINT_CaNo_3 = MINT_Deficit_CaNo_9,
         MINT_Olfa_1 = MINT_Deficit_Olfa_10,
         MINT_Olfa_2 = MINT_Deficit_Olfa_11,
         MINT_Olfa_3 = MINT_Deficit_Olfa_12,
         MINT_Sati_1 = MINT_Deficit_Sati_13,
         MINT_Sati_2 = MINT_Deficit_Sati_14,
         MINT_Sati_3 = MINT_Deficit_Sati_15,
         MINT_SexS_1 = MINT_Awareness_SexS_19,
         MINT_SexS_2 = MINT_Awareness_SexS_20,
         MINT_SexS_3 = MINT_Awareness_SexS_21,
         MINT_SexO_1 = MINT_Awareness_SexO_22,
         MINT_SexO_2 = MINT_Awareness_SexO_23,
         MINT_SexO_3 = MINT_Awareness_SexO_24,
         MINT_UrSe_1 = MINT_Awareness_UrSe_25,
         MINT_UrSe_2 = MINT_Awareness_UrSe_26,
         MINT_UrSe_3 = MINT_Awareness_UrSe_27,
         MINT_RelA_1 = MINT_Awareness_RelA_28,
         MINT_RelA_2 = MINT_Awareness_RelA_29,
         MINT_RelA_3 = MINT_Awareness_RelA_30,
         MINT_StaS_1 = MINT_Awareness_StaS_31,
         MINT_StaS_2 = MINT_Awareness_StaS_32,
         MINT_StaS_3 = MINT_Awareness_StaS_33,
         MINT_ExAc_1 = MINT_Awareness_ExAc_34,
         MINT_ExAc_2 = MINT_Awareness_ExAc_35,
         MINT_ExAc_3 = MINT_Awareness_ExAc_36,
         MINT_Card_1 = MINT_Sensitivity_Card_37,
         MINT_Card_2 = MINT_Sensitivity_Card_38,
         MINT_Card_3 = MINT_Sensitivity_Card_39,
         MINT_Resp_1 = MINT_Sensitivity_Resp_40,
         MINT_Resp_2 = MINT_Sensitivity_Resp_41,
         MINT_Resp_3 = MINT_Sensitivity_Resp_42,
         MINT_Gast_1 = MINT_Sensitivity_Gast_46,
         MINT_Gast_2 = MINT_Sensitivity_Gast_47,
         MINT_Gast_3 = MINT_Sensitivity_Gast_48,
         MINT_Derm_1 = MINT_Sensitivity_Derm_49,
         MINT_Derm_2 = MINT_Sensitivity_Derm_50,
         MINT_Derm_3 = MINT_Sensitivity_Derm_51)

colors <- c(Mint="#00BCD4", metaMint="#673AB7", miniMint="#26A36C", microMint="#8BC34A", 
            IAS="#F44336", MAIA="#FFAD04",  iMAIA="#FF7811", BPQ="#795548")

Structure Validation

items <- select(df, starts_with("MINT_"))
names(items) <- str_remove(names(items), "MINT_")

labels <- list(
  MINT_Urin_1 = "I sometimes feel like I need to urinate or defecate but when I go to the bathroom I produce less than I expected",
  MINT_Urin_2 = "I often feel the need to urinate even when my bladder is not full",
  MINT_Urin_3 = "Sometimes I am not sure whether I need to go to the toilet or not (to urinate or defecate)",
  MINT_CaCo_1 = "Sometimes my breathing becomes erratic or shallow and I often don't know why",
  MINT_CaCo_2 = "I often feel like I can't get enough oxygen by breathing normally",
  MINT_CaCo_3 = "Sometimes my heart starts racing and I often don't know why",
  MINT_CaNo_1 = "I often only notice how I am breathing when it becomes loud",
  MINT_CaNo_2 = "I only notice my heart when it is thumping in my chest",
  MINT_CaNo_3 = "I often only notice how I am breathing when my breathing becomes shallow or irregular",
  MINT_Olfa_1 = "I often check the smell of my armpits",
  MINT_Olfa_2 = "I often check the smell of my own breath",
  MINT_Olfa_3 = "I often check the smell of my farts",
  MINT_Sati_1 = "I don't always feel the need to eat until I am really hungry",
  MINT_Sati_2 = "Sometimes I don't realise I was hungry until I ate something",
  MINT_Sati_3 = "I don't always feel the need to drink until I am really thirsty",
  # MINT_SexA_16 = "I always feel in my body if I am sexually aroused",
  # MINT_SexA_17 = "I can always tell that I am sexually aroused from the way I feel inside",
  # MINT_SexA_18 = "I always know when I am sexually aroused",
  MINT_SexS_1 = "During sex or masturbation, I often feel very strong sensations coming from my genital areas",
  MINT_SexS_2 = "When I am sexually aroused, I often notice specific sensations in my genital area (e.g., tingling, warmth, wetness, stiffness, pulsations)",
  MINT_SexS_3 = "My genital organs are very sensitive to pleasant stimulations",
  MINT_SexO_1 = "In general, I am very sensitive to changes in my genital organs",
  MINT_SexO_2 = "I can notice even very subtle changes in the state of my genital organs",
  MINT_SexO_3 = "I am always very aware of the state of my genital organs, even when I am calm",
  MINT_UrSe_1 = "In general, I am very aware of the sensations that are happening when I am urinating",
  MINT_UrSe_2 = "In general, I am very aware of the sensations that are happening when I am defecating",
  MINT_UrSe_3 = "I often experience a pleasant sensation when relieving myself when urinating or defecating)",
  MINT_RelA_1 = "I always know when I am relaxed",
  MINT_RelA_2 = "I always feel in my body if I am relaxed",
  MINT_RelA_3 = "My body is always in the same specific state when I am relaxed",
  MINT_StaS_1 = "Being relaxed is a very different bodily feeling compared to other states (e.g., feeling anxious, sexually aroused or after exercise)",
  MINT_StaS_2 = "Being sexually aroused is a very different bodily feeling compared to other states (e.g., feeling anxious, relaxed, or after physical exercise)",
  MINT_StaS_3 = "Being anxious is a very different bodily feeling compared to other states (e.g., feeling sexually aroused, relaxed or after exercise)",
  MINT_ExAc_1 = "I can always accurately feel when I am about to burp",
  MINT_ExAc_2 = "I can always accurately feel when I am about to fart",
  MINT_ExAc_3 = "I can always accurately feel when I am about to sneeze",
  MINT_Card_1 = "In general, I am very sensitive to changes in my heart rate",
  MINT_Card_2 = "I can notice even very subtle changes in the way my heart beats",
  MINT_Card_3 = "I often notice changes in my heart rate",
  MINT_Resp_1 = "I can notice even very subtle changes in my breathing",
  MINT_Resp_2 = "I am always very aware of how I am breathing, even when I am calm",
  MINT_Resp_3 = "In general, I am very sensitive to changes in my breathing",
  # MINT_Sign_43 = "When something important is happening in my life, I can feel immediately feel changes in my heart rate",
  # MINT_Sign_44 = "When something important is happening in my life, I can immediately feel changes in my breathing",
  # MINT_Sign_45 = "When something important is happening in my life, I can feel it in my body",
  MINT_Gast_1 = "I can notice even very subtle changes in what my stomach is doing",
  MINT_Gast_2 = "In general, I am very sensitive to what my stomach is doing",
  MINT_Gast_3 = "I am always very aware of what my stomach is doing, even when I am calm",
  MINT_Derm_1 = "In general, my skin is very sensitive",
  MINT_Derm_2 = "My skin is susceptible to itchy fabrics and materials",
  MINT_Derm_3 = "I can notice even very subtle stimulations to my skin (e.g., very light touches)"
  # MINT_SexC_52 = "When I am sexually aroused, I often feel changes in the way my heart beats (e.g., faster or stronger)",
  # MINT_SexC_53 = "When I am sexually aroused, I often feel changes in my breathing (e.g., faster, shallower, or less regular)",
  # MINT_SexC_54 = "When I am sexually aroused, I often feel changes in my temperature (e.g., feeling warm or cold)"
)


cleanlabels <- function(x, qname=FALSE) {
  is_factor <- FALSE
  if(is.factor(x)) {
    is_factor <- TRUE
    vec <- x
    x <- levels(x)
  }
  
  x <- x |> 
    str_replace("MINT_", ifelse(qname, "MINT - ", "")) |> 
    str_replace("MAIA_", ifelse(qname, "MAIA - ", "")) |> 
    str_replace("_", " - ") 
  
  if(is_factor) {
    levels(vec) <- x
    return(vec)
  }
  x
}

Full

EGA

ega1 <- bootEGA(
  items,
  EGA.type = "hierEGA",
  model = "glasso",  # BGGM
  algorithm = "leiden",
  allow.singleton = TRUE,
  type="resampling",
  plot.itemStability=FALSE,
  typicalStructure=TRUE,
  plot.typicalGraph=FALSE,
  iter=500,
  seed=3, ncores = 4)

save(ega1, file="models/ega1.RData")

load("models/ega1.RData")

itemstability <- EGAnet::itemStability(ega1, IS.plot=FALSE)
plot(itemstability)

Hierarchical Clustering

hclust <- pvclust::pvclust(items,
                 method.hclust = "ward.D2",
                 method.dist = "correlation",
                 nboot = 1000, quiet=TRUE, parallel=TRUE)
plot(hclust, hang = -1, cex = 0.5)
pvclust::pvrect(hclust, alpha=0.95, max.only=TRUE)

Item Reduction

R2

rbind(rez, grandave) |> 
  select(-Group, -Rank) |> 
  pivot_wider(names_from = "Outcome", values_from = "R2") |> 
  mutate(Group = case_when(
    Dimension %in% c("Card", "Resp", "Gast", "CardRespGast", "CardResp", "CardGast") ~ "Visceral", 
    Dimension %in% c("RelA", "SexS", "ExaC", "StaS", "SexO", "UrSe") ~ "sensitivity", 
    Dimension %in% c("CaCo", "Urin", "Derm", "Olfa", "Sati", "CaNo") ~ "Deficit", 
    .default = "Other")) |> 
  select(Group, Dimension, Average, Interoception, Biofeedback, ER, 
         Primals, Alexithymia, Dissociative, Mood, 
         Physical, Mental, Lifestyle) |>
  arrange(Group, desc(Average)) |> 
  # select(-Group) |> 
  gt::gt() |> 
  gt::data_color(
    columns = -c(Dimension, Group),
    palette = c("white", "green"),
    domain = c(0, 1)
  ) |>
  gt::fmt_number() |>
  gt::tab_header(
    title = "Variable Importance",
    subtitle = "Normalized Predictive Power (R2)"
  ) |> 
  gt::tab_row_group(
    label = "Sensitivity",
    rows = Group == "sensitivity"
  )  |> 
  gt::tab_row_group(
    label = "Interoception",
    rows = Group == "Visceral"
  ) |> 
  gt::tab_row_group(
    label = "Deficit",
    rows = Group == "Deficit"
  ) |> 
  gt::cols_hide(Group)

Variable Importance
Normalized Predictive Power (R2)
Dimension	Average	Interoception	Biofeedback	ER	Primals	Alexithymia	Dissociative	Mood	Physical	Mental	Lifestyle
Deficit
CaCo	0.62	0.10	0.05	0.32	0.77	1.00	1.00	1.00	0.64	0.72	0.76
Urin	0.43	0.09	0.20	0.07	0.54	0.64	0.52	0.61	0.74	0.45	0.22
Derm	0.32	0.06	0.01	0.15	0.35	0.29	0.20	0.34	0.87	0.59	0.08
Olfa	0.25	0.09	0.42	0.22	0.46	0.29	0.16	0.22	0.03	0.20	0.00
Sati	0.24	0.01	0.01	0.10	0.31	0.56	0.36	0.20	0.06	0.23	0.53
CaNo	0.05	0.01	0.01	0.05	0.00	0.10	0.01	0.06	0.02	0.03	0.20
Interoception
CardRespGast	0.32	0.78	0.81	0.26	0.11	0.04	0.08	0.19	0.28	0.22	0.01
CardResp	0.31	0.70	0.80	0.19	0.08	0.05	0.09	0.21	0.20	0.13	0.01
CardGast	0.28	0.62	0.73	0.19	0.10	0.03	0.07	0.19	0.36	0.23	0.01
Card	0.28	0.48	0.67	0.06	0.07	0.05	0.09	0.23	0.22	0.13	0.00
Resp	0.25	0.70	0.62	0.26	0.07	0.04	0.06	0.13	0.10	0.15	0.03
Gast	0.17	0.43	0.26	0.17	0.08	0.00	0.02	0.06	0.12	0.07	0.03
Sensitivity
RelA	0.54	0.77	0.49	0.92	0.79	0.57	0.47	0.29	0.21	0.22	0.51
SexS	0.23	0.28	0.54	0.07	0.22	0.15	0.15	0.03	0.02	0.08	0.41
UrSe	0.21	0.36	0.66	0.40	0.14	0.01	0.00	0.00	0.03	0.02	0.07
ExaC	0.20	0.16	0.12	0.11	0.33	0.10	0.09	0.03	0.04	0.17	0.08
SexO	0.18	0.43	0.48	0.23	0.03	0.00	0.00	0.01	0.00	0.02	0.19
StaS	0.16	0.21	0.13	0.04	0.10	0.16	0.28	0.02	0.02	0.08	0.18

Rank

rbind(rez, grandave) |> 
  select(-Group, -R2) |> 
  pivot_wider(names_from = "Outcome", values_from = "Rank") |> 
  mutate(Group = case_when(
    Dimension %in% c("Card", "Resp", "Gast", "CardRespGast", "CardResp", "CardGast") ~ "Visceral", 
    Dimension %in% c("RelA", "SexS", "ExaC", "StaS", "SexO", "UrSe") ~ "sensitivity", 
    Dimension %in% c("CaCo", "Urin", "Derm", "Olfa", "Sati", "CaNo") ~ "Deficit", 
    .default = "Other")) |> 
  datawizard::data_relocate(select=c("Group", "Dimension", "Average", "Interoception")) |>
  arrange(Group, Average) |> 
  gt::gt() |> 
  # gt::fmt_number(columns = "Average") |>
  gt::data_color(
    columns = -c(Dimension, Group),
    palette = c("green", "white"),
    domain = c(1, 18)
  ) |> 
  gt::opt_interactive(page_size_default=20)

Clusters

hclust2 <- rez |>
  filter(Group != Outcome) |>
  select(-Group, -Rank) |>
  pivot_wider(names_from = "Dimension", values_from = "R2") |>
  select(-Outcome) |>
  pvclust::pvclust(
     method.hclust = "average",
     method.dist = "correlation",
     nboot = 1000, quiet=TRUE, parallel=TRUE)

plot(hclust2, hang = -1, cex = 0.5)
pvclust::pvrect(hclust2, alpha=0.95, max.only=TRUE)

Final

items <- items |> 
  select(
    -contains("UrSe"), # Unstable item
    -contains("StaS"), # Too vague and overlapping
    -contains("SexO"), # Overalapping with SexS
    
    -contains("CaNo") # Overlap with CaCo
    # -contains("Olfa"), # Unstable metacluster
    # -contains("Sati")
  )

EGA

ega2 <- bootEGA(
  items,
  EGA.type = "hierEGA",
  model = "glasso",  # BGGM
  algorithm = "leiden",
  # consensus.method = "lowest_tefi",
  allow.singleton = TRUE,
  type="resampling",
  plot.itemStability=FALSE,
  typicalStructure=TRUE,
  plot.typicalGraph=FALSE,
  iter=500,
  seed=3, ncores = 4)

save(ega2, file="models/ega2.RData")

load("models/ega2.RData")

# EGAnet::dimensionStability(ega)
itemstability <- EGAnet::itemStability(ega2, IS.plot=FALSE)
p_stab <- plot(itemstability)

p_stab

Item Table

make_loadingtable <- function(x) {
  t <- as.data.frame(x) |>
    datawizard::data_addprefix("C")
  t$Cluster <- colnames(t)[max.col(t, ties.method='first')]

  t |>
    rownames_to_column(var="Item") |>
    rowwise() |>
    mutate(Max = max(c_across(-c(Item, Cluster)))) |>
    arrange(Cluster, desc(Max)) |>
    as.data.frame()
}

dimensions <- list(
  C01 = "SexS",       # Sexual Arousal Sensitivity
  C02 = "Derm",       # Dermal Hypersensitivity
  C03 = "Card",       # Cardioception
  C04 = "Sati",       # Satiety Awareness
  C05 = "Gast",       # Gastroception
  C06 = "Olfa",       # Olfactory Compensation
  C07 = "Resp",       # Respiroception
  C08 = "RelA",       # Relaxation Awareness
  C09 = "Urin",       # Urointestinal Inaccuracy
  C10 = "ExAc",       # Expulsion Accuracy
  C11 = "CaCo"       # Cardiorespiratory Confusion
  # C01 = "C01",
  # C02 = "C02",
  # C03 = "C03",
  # C04 = "C04",
  # C05 = "C05",
  # C06 = "C06",
  # C07 = "C07",
  # C08 = "C08",
  # C09 = "C09",
  # C10 = "C10",
  # C11 = "C11",
  # C12 = "C12"
)


higher <- make_loadingtable(t(net.loads(ega2$EGA$higher_order, loading.method="revised")$std)) |> 
  arrange(Item)
cluster_order <- names(higher)[!names(higher) %in% c("Item", "Cluster", "Max")]


lower <- make_loadingtable(net.loads(ega2$EGA$lower_order, loading.method="revised")$std) |>
  select(names(higher)) |>
  mutate(Cluster = fct_relevel(Cluster, cluster_order)) |>
  arrange(Cluster, desc(Max))
dimensions <- dimensions[names(dimensions) %in% names(lower)]

higher$Item <- paste0("M", higher$Item)
# higher$Label <- c("Interoceptive Deficit", "Interoceptive Awareness", "Interoceptive Sensitivity")
higher$Label <- c("Interoceptive Awareness", "Interoceptive Deficit", "Visceroception")
higher$ID  <- ""

lower <- mutate(lower, Label = labels[paste0("MINT_", Item)], ID=Item, Item = 1:n())

tab1 <- rbind(higher, lower) |>
  select(-ID) |>
  datawizard::data_relocate("Label", after = "Item") |>
  datawizard::data_rename(names(dimensions), dimensions[names(dimensions)]) |>
  select(-Cluster, -Max) |> 
  mutate(`|` = "") |> 
  gt::gt() |>
  gt::tab_row_group(
    label = "Items",
    rows = 1:(nrow(lower) + nrow(higher))
  ) |>
  gt::tab_row_group(
    label = "Metaclusters",
    rows = 1:nrow(higher)
  ) |>
  gt::data_color(columns=-Item,
                 method = "numeric",
                 palette = c("red", "white", "green"),
                 domain = c(-1, 1)) |>
  gt::tab_style(
    style = gt::cell_text(size="small", style="italic"),
    locations = gt::cells_body(columns="Label", rows=c(4:36))
  ) |>
  gt::tab_style(
    style = list(gt::cell_text(weight="bold"),
                 gt::cell_fill(color = "#F5F5F5")),
    locations = list(
      gt::cells_row_groups(groups = "Items"),
      gt::cells_row_groups(groups = "Metaclusters")
    )
  ) |>
  gt::fmt_number(columns=-Item, decimals=2) |>
  gt::tab_header(
    title = gt::md("**Item Loadings**"),
    subtitle = "Node centrality"
  ) |> 
  # Metacluster 1
  gt::tab_style(
    style = gt::cell_fill(color = "#81C784"),
    locations = list(
      gt::cells_body(columns="Item", rows=c(1))
    )
  ) |>
  gt::tab_style(
    style = gt::cell_fill(color = "#81C784"),
    locations = list(
      gt::cells_column_labels(columns = 3),
      gt::cells_body(columns="Item", rows=c(4:6))
    )
  ) |>
  gt::tab_style(
    style = gt::cell_fill(color = "#AED581"),
    locations = list(
      gt::cells_column_labels(columns = 4),
      gt::cells_body(columns="Item", rows=c(7:9))
    )
  ) |>
  gt::tab_style(
    style = gt::cell_fill(color = "#DCE775"),
    locations = list(
      gt::cells_column_labels(columns = 5),
      gt::cells_body(columns="Item", rows=c(10:12))
    )
  ) |> 
  gt::tab_style(
    style = gt::cell_fill(color = "#BA68C8"),
    locations = list(
      gt::cells_body(columns="Item", rows=c(2))
    )
  ) |> 
  gt::tab_style(
    style = gt::cell_fill(color = "#BA68C8"),
    locations = list(
      gt::cells_column_labels(columns = 6),
      gt::cells_body(columns="Item", rows=c(13:15))
    )
  ) |>
  gt::tab_style(
    style = gt::cell_fill(color = "#9575CD"),
    locations = list(
      gt::cells_column_labels(columns = 7),
      gt::cells_body(columns="Item", rows=c(16:18))
    )
  ) |>
  gt::tab_style(
    style = gt::cell_fill(color = "#7986CB"),
    locations = list(
      gt::cells_column_labels(columns = 8),
      gt::cells_body(columns="Item", rows=c(19:21))
    )
  ) |>
  gt::tab_style(
    style = gt::cell_fill(color = "#64B5F6"),
    locations = list(
      gt::cells_column_labels(columns = 9),
      gt::cells_body(columns="Item", rows=c(22:24))
    )
  ) |>
  gt::tab_style(
    style = gt::cell_fill(color = "#4DD0E1"),
    locations = list(
      gt::cells_column_labels(columns = 10),
      gt::cells_body(columns="Item", rows=c(25:27))
    )
  ) |>
  gt::tab_style(
    style = gt::cell_fill(color = "#FF7811"),
    locations = list(
      gt::cells_body(columns="Item", rows=c(3))
    )
  ) |> 
  gt::tab_style(
    style = gt::cell_fill(color = "#FFB74D"),
    locations = list(
      gt::cells_column_labels(columns = 11),
      gt::cells_body(columns="Item", rows=c(28:30))
    )
  ) |>
  gt::tab_style(
    style = gt::cell_fill(color = "#E57373"),
    locations = list(
      gt::cells_column_labels(columns = 12),
      gt::cells_body(columns="Item", rows=c(31:33))
    )
  ) |> 
  gt::tab_style(
    style = gt::cell_fill(color = "#A1887F"),
    locations = list(
      gt::cells_column_labels(columns = 13),
      gt::cells_body(columns="Item", rows=c(34:36))
    )
  ) 
tab1

Item Loadings
Node centrality
Item	Label	ExAc	RelA	SexS	CaCo	Urin	Derm	Sati	Olfa	Resp	Card	Gast	|
Metaclusters
M1	Interoceptive Awareness	0.42	0.41	0.30	−0.20	−0.06	0.06	−0.07	0.08	0.13	0.02	0.12
M2	Interoceptive Deficit	0.00	−0.17	0.01	0.49	0.44	0.24	0.23	0.18	0.21	0.20	0.12
M3	Visceroception	0.02	0.15	0.02	0.20	0.01	0.11	0.00	0.13	0.60	0.58	0.33
Items
1	I can always accurately feel when I am about to fart	0.48	0.02	0.05	0.00	0.00	0.04	−0.01	0.10	0.00	0.00	0.00
2	I can always accurately feel when I am about to sneeze	0.48	0.09	0.01	0.00	0.00	0.04	0.00	0.00	0.00	0.00	0.00
3	I can always accurately feel when I am about to burp	0.46	0.06	0.01	−0.02	−0.04	0.05	−0.06	0.00	0.03	0.00	0.00
4	I always feel in my body if I am relaxed	0.02	0.59	0.02	−0.04	0.00	0.00	−0.01	0.00	0.04	0.00	0.03
5	I always know when I am relaxed	0.10	0.58	0.03	−0.10	−0.07	0.00	−0.03	−0.01	0.03	0.03	0.02
6	My body is always in the same specific state when I am relaxed	0.04	0.28	0.00	−0.01	0.00	0.00	0.00	0.00	0.00	0.03	0.00
7	During sex or masturbation, I often feel very strong sensations coming from my genital areas	0.01	0.03	0.65	−0.05	−0.04	0.00	0.00	0.00	0.00	0.00	0.00
8	My genital organs are very sensitive to pleasant stimulations	0.00	0.02	0.53	0.00	0.00	0.02	0.00	0.01	0.00	0.00	0.00
9	When I am sexually aroused, I often notice specific sensations in my genital area (e.g., tingling, warmth, wetness, stiffness, pulsations)	0.08	0.02	0.53	0.00	0.01	0.03	0.00	0.02	0.00	0.01	0.02
10	Sometimes my breathing becomes erratic or shallow and I often don't know why	−0.02	−0.01	0.00	0.68	0.11	0.00	0.09	0.02	0.07	0.00	0.00
11	I often feel like I can't get enough oxygen by breathing normally	0.00	−0.07	−0.04	0.39	0.04	0.04	0.02	0.02	0.06	0.04	0.00
12	Sometimes my heart starts racing and I often don't know why	0.00	−0.07	0.00	0.37	0.06	0.05	0.03	0.00	0.00	0.16	0.00
13	I sometimes feel like I need to urinate or defecate but when I go to the bathroom I produce less than I expected	0.00	0.00	0.01	0.08	0.63	0.03	0.01	0.07	0.00	0.00	0.00
14	I often feel the need to urinate even when my bladder is not full	0.00	−0.01	0.00	0.04	0.44	0.09	0.00	0.02	0.00	0.00	0.01
15	Sometimes I am not sure whether I need to go to the toilet or not (to urinate or defecate)	−0.04	−0.06	−0.03	0.09	0.32	0.00	0.12	0.00	0.00	0.00	0.00
16	In general, my skin is very sensitive	0.00	0.00	0.00	0.01	0.00	0.70	0.02	0.00	0.04	0.00	0.01
17	My skin is susceptible to itchy fabrics and materials	0.00	0.00	0.00	0.07	0.07	0.46	0.01	0.01	0.00	0.00	0.01
18	I can notice even very subtle stimulations to my skin (e.g., very light touches)	0.13	0.00	0.04	0.00	0.05	0.30	0.00	0.00	0.02	0.03	0.04
19	I don't always feel the need to eat until I am really hungry	−0.01	0.00	0.00	0.00	0.00	0.01	0.60	0.00	0.00	0.00	0.00
20	Sometimes I don't realise I was hungry until I ate something	−0.05	−0.02	0.00	0.09	0.10	0.01	0.49	0.02	0.01	0.00	0.00
21	I don't always feel the need to drink until I am really thirsty	0.00	−0.03	0.00	0.04	0.03	0.00	0.23	0.03	0.00	−0.01	0.00
22	I often check the smell of my armpits	0.00	−0.01	0.00	0.02	0.03	0.00	0.04	0.59	0.04	0.03	0.00
23	I often check the smell of my own breath	0.00	0.00	0.01	0.02	0.00	0.00	0.02	0.55	0.04	0.00	0.04
24	I often check the smell of my farts	0.10	0.00	0.01	0.00	0.06	0.01	0.00	0.28	0.00	0.00	0.01
25	In general, I am very sensitive to changes in my breathing	0.00	0.04	0.00	0.06	0.00	0.07	0.00	0.02	0.54	0.15	0.02
26	I can notice even very subtle changes in my breathing	0.03	0.03	0.00	0.03	0.00	0.00	0.00	0.01	0.54	0.14	0.02
27	I am always very aware of how I am breathing, even when I am calm	0.00	0.02	0.00	0.04	0.00	0.00	0.01	0.04	0.43	0.05	0.06
28	In general, I am very sensitive to changes in my heart rate	0.00	0.00	0.00	0.07	0.00	0.03	−0.01	0.00	0.11	0.55	0.03
29	I often notice changes in my heart rate	0.00	0.00	0.01	0.14	0.00	0.00	0.00	0.03	0.08	0.53	0.01
30	I can notice even very subtle changes in the way my heart beats	0.00	0.07	0.00	0.01	0.00	0.00	0.00	0.00	0.17	0.45	0.05
31	I can notice even very subtle changes in what my stomach is doing	0.00	0.02	0.00	0.00	0.01	0.04	0.00	0.03	0.05	0.04	0.59
32	In general, I am very sensitive to what my stomach is doing	0.00	0.00	0.01	0.00	0.00	0.02	0.00	0.02	0.00	0.02	0.58
33	I am always very aware of what my stomach is doing, even when I am calm	0.00	0.04	0.01	0.00	0.00	0.00	0.00	0.00	0.07	0.04	0.53

gt::gtsave(tab1, "figures/table1.png", vwidth=1660, expand=0, quiet=TRUE)

Hierarchical Clustering

rez <- pvclust::pvclust(items,
                 method.hclust = "ward.D2",
                 method.dist = "correlation",
                 nboot = 1000, quiet=TRUE, parallel=TRUE)
plot(rez, hang = -1, cex = 0.5)
pvclust::pvrect(rez, alpha=0.95, max.only=FALSE)

dendrogram <- as.dist(1 - cor(items, use = "pairwise.complete.obs")) |> 
  hclust(method = "ward.D2") |> 
  tidygraph::as_tbl_graph() 


# Process Nodes
nodes <- as.list(dendrogram)$nodes |> 
  mutate(Group = case_when(str_detect(label, "Card") ~ "Card", 
                           str_detect(label, "Resp") ~ "Resp",
                           str_detect(label, "Gast") ~ "Gast",
                           str_detect(label, "SexS") ~ "SexS",
                           str_detect(label, "RelA") ~ "RelA",
                           str_detect(label, "ExAc") ~ "ExAc",
                           str_detect(label, "Urin") ~ "Urin",
                           str_detect(label, "CaCo") ~ "CaCo",
                           str_detect(label, "Derm") ~ "Derm",
                           str_detect(label, "Olfa") ~ "Olfa",
                           str_detect(label, "Sati") ~ "Sati",
                           label == "" ~ "NA",
                           .default = "Other"),
         Size = setNames(lower$Max, lower$ID)[label],
         item = label)
nodes$idx <- 1:nrow(nodes)

# Rename accoring to table
items_newnames <- lower |>
  separate(ID, into = c("Group", "Item"), sep = "_", remove = FALSE) |>
  mutate(Name = paste0(Group, "-", 1:nrow(lower)))
items_newnames <- setNames(items_newnames$Name, items_newnames$ID)
  
nodes$label <- items_newnames[nodes$label]
nodes$label <- ifelse(is.na(nodes$label), "", nodes$label)

# Central node 
max_size  <- max(nodes$Size, na.rm = TRUE)
nodes[nodes$height == max(nodes$height), c("Group", "Size")] <- data.frame(Group="Central", Size=2 * max_size)
# Metaclusters
nodes[17, c("Group", "Size")] <- data.frame(Group="Awareness", Size=1.5 * max_size)
nodes[34, c("Group", "Size")] <- data.frame(Group="Visceroception", Size=1.5 * max_size)
nodes[63, c("Group", "Size")] <- data.frame(Group="Deficit", Size=1.5 * max_size)


# Process Edges
edges <- as.list(dendrogram)$edges
edges$linewidth = datawizard::rescale(nodes[edges$from, ]$height, to = c(0.1, 1))

p_hclust <- tbl_graph(nodes = nodes, edges = edges) |> 
  ggraph(layout = "dendrogram", circular = TRUE) +
  # geom_edge_diagonal(strength = 0.7, linewidth = 1) +
  geom_edge_elbow2(aes(edge_width=linewidth), color="darkgrey") +
  geom_node_point(aes(filter=Group %in% c("Central"), size = Size), color="darkgrey") +
  geom_node_point(aes(filter=Group %in% c("Visceroception", "Awareness", "Deficit"), color=Group, size = Size)) +
  geom_node_text(aes(
    label = ifelse(label != "NA", label, NA),
    x = x * 1.05,
    y = y * 1.05,
    filter = label != "",
    angle = ifelse(
      x >= 0,
      asin(y) * 360 / 2 / pi,
      360 - asin(y) * 360 / 2 / pi
    ),
    hjust = ifelse(
      x >= 0, 0, 1
    ))) +
  geom_node_point(aes(filter = label != "", color=Group, size=Size), alpha = 1) +
  # geom_node_text(aes(label=idx)) +  # Debug
  scale_edge_width_continuous(range=c(1, 3), guide = "none") +
  scale_size_continuous(range=c(3, 11), guide = "none") +
  scale_color_manual(values = c(
    "Visceroception" = "#FF7811", "Card" = "#F44336", "Resp"="#FF9800", "Gast"="#795548",
    "Awareness" = "#4CAF50", "ExAc"="#4CAF50", "RelA"="#8BC34A", "SexS" = "#CDDC39",
    "Deficit" = "#9C27B0", "CaCo"="#9C27B0", "Urin" = "#673AB7", "Derm"="#3F51B5",
    "Sati" = "#2196F3", "Olfa" = "#00BCD4"),
                     breaks = c(
                       "Awareness", "ExAc", "RelA", "SexS",
                       "Deficit", "CaCo", "Urin", "Derm", "Sati", "Olfa",
                       "Visceroception", "Resp", "Card", "Gast"
                       ),
                     labels=c(
                       "***Awareness***", "ExAc", "RelA", "SexS",
                       "***Deficit***", "CaCo", "Urin", "Derm", "Sati", "Olfa",
                       "***Visceroception***", "Resp", "Card", "Gast"
                       )) +
  ggtitle("Hierarchical Clustering", subtitle = "Method = Correlation") +
  coord_equal(clip = "off", xlim = c(-1.25, 1.25), ylim = c(-1.25, 1.25)) +
  theme_void() + 
  guides(color = guide_legend(override.aes = list(size = c(7.5, 5, 5, 3.5, 7.5, 5, 5, 3.5, 3.5, 3.5, 7.5, 5, 5, 3.5)))) +
  theme(legend.text = ggtext::element_markdown(),
        legend.title = element_blank(),
        plot.title = element_blank(),  #element_text(face="bold")
        plot.subtitle = element_blank())
p_hclust

ggsave("figures/fig1a.png", p_stab, width=10, height=10*0.8, dpi=300)
ggsave("figures/fig1b.png", p_hclust, width=12, height=8, dpi=300)

EFA

n <- parameters::n_factors(items)
plot(n)

fa <- parameters::factor_analysis(items, n = 9, rotation = "varimax", sort = TRUE)
plot(fa)

# fa |>
#   select(Variable, Uniqueness) |>
#   mutate(Group = str_extract(Variable, "^[A-Z][a-z]+")) |>
#   mutate(Average = mean(Uniqueness), .by = "Group") |> 
#   arrange(desc(Average), desc(Uniqueness)) |> 
#   select(-Average, -Group) |> 
#   gt::gt() |> 
#   gt::data_color(
#     columns = "Uniqueness",
#     palette = c("red", "white", "green"),
#     domain = c(0, 1) 
#   ) 

Score Extraction

# Awareness
df$MINT_SexS <- rowMeans(select(df, starts_with("MINT_SexS_")), na.rm = TRUE)
df$MINT_ExaC <- rowMeans(select(df, starts_with("MINT_ExaC_")), na.rm = TRUE)
df$MINT_RelA <- rowMeans(select(df, starts_with("MINT_RelA_")), na.rm = TRUE)
# df$MINT_StaS <- rowMeans(select(df, starts_with("MINT_StaS_")), na.rm = TRUE)
# df$MINT_SexO <- rowMeans(select(df, starts_with("MINT_SexO_")), na.rm = TRUE)
# df$MINT_UrSe <- rowMeans(select(df, starts_with("MINT_UrSe_")), na.rm = TRUE)

# Deficit
df$MINT_Urin <- rowMeans(select(df, starts_with("MINT_Urin_")), na.rm = TRUE)
df$MINT_CaCo <- rowMeans(select(df, starts_with("MINT_CaCo_")), na.rm = TRUE)
df$MINT_Derm <- rowMeans(select(df, starts_with("MINT_Derm_")), na.rm = TRUE)
# df$MINT_CaNo <- rowMeans(select(df, starts_with("MINT_CaNo_")), na.rm = TRUE)

df$MINT_Sati <- rowMeans(select(df, starts_with("MINT_Sati_")), na.rm = TRUE)
df$MINT_Olfa <- rowMeans(select(df, starts_with("MINT_Olfa_")), na.rm = TRUE)

# Visceral
df$MINT_Resp <- rowMeans(select(df, starts_with("MINT_Resp_")), na.rm = TRUE)
df$MINT_Card <- rowMeans(select(df, starts_with("MINT_Card_")), na.rm = TRUE)
df$MINT_Gast <- rowMeans(select(df, starts_with("MINT_Gast_")), na.rm = TRUE)

df <- df[names(df)[!grepl("MINT_.*[0-9]$", names(df))]]

Mini Versions

mint <- select(df, starts_with("MINT_"))

metamint <- data.frame(
  MINT_Awareness = rowMeans(select(df, MINT_SexS, MINT_ExaC, MINT_RelA)),
  MINT_Deficit = rowMeans(select(df, MINT_CaCo, MINT_Urin, MINT_Derm, MINT_Sati, MINT_Olfa)),
  MINT_Visceroception = rowMeans(select(df, MINT_Card, MINT_Resp, MINT_Gast))
)

minimint <- data.frame(
  MINT_Awareness = rowMeans(select(df, MINT_ExaC, MINT_RelA)),
  MINT_Deficit = rowMeans(select(df, MINT_CaCo, MINT_Urin)),
  MINT_Visceroception = rowMeans(select(df, MINT_Resp, MINT_Card))
)

micromint <- data.frame(
  MINT_ExaC = df$MINT_ExaC,
  MINT_CaCo = df$MINT_CaCo,
  MINT_Resp = df$MINT_Resp
)

make_cor <- function(df1, df2=NULL, qname_x=TRUE, qname_y=TRUE, textsize = 2.9, xcol = "#424242", ycol="#424242") {
  
  r <- df1 |> 
    correlation::correlation(data2=df2, p_adjust="none", redundant=ifelse(is.null(df2), TRUE, FALSE)) |> 
    correlation::cor_sort() |>
    mutate(label = ifelse(p < .001, paste0(insight::format_value(r)), ""),
           Parameter1 = cleanlabels(Parameter1, qname=qname_x),
           Parameter2 = cleanlabels(Parameter2, qname=qname_y))
  r[as.character(r$Parameter1) == as.character(r$Parameter2), "label"] <- ""
  
  xnames <- levels(r$Parameter1)
  ynames <- levels(r$Parameter2)
  
  xcol <- case_when(
    str_detect(xnames, "MINT") ~ "#00838F",
    str_detect(xnames, "MAIA") ~ "#EF6C00",
    str_detect(xnames, "IAS") ~ "#C62828",
    str_detect(xnames, "BPQ") ~ "#795548",
    .default = xcol
  )
  ycol <- case_when(
    str_detect(ynames, "MINT") ~ "#00838F",
    str_detect(ynames, "MAIA") ~ "#EF6C00",
    str_detect(ynames, "IAS") ~ "#C62828",
    str_detect(ynames, "BPQ") ~ "#795548",
    .default = ycol
  )
  xbold <- case_when(
    xnames %in% c("Awareness", "MINT - Awareness") ~ "bold",
    str_detect(xnames, "Visceroception") ~ "bold",
    str_detect(xnames, "Deficit") ~ "bold",
    .default = "plain"
  )
  ybold <- case_when(
    ynames %in% c("Awareness", "MINT - Awareness") ~ "bold",
    str_detect(ynames, "Visceroception") ~ "bold",
    str_detect(ynames, "Deficit") ~ "bold",
    .default = "plain"
  )
  
  r |> 
    ggplot(aes(x=Parameter1, y=Parameter2, fill=r)) +
    geom_tile() +
    geom_text(aes(label=label), size=textsize) +
    scale_fill_gradient2(low="blue", high="red", mid="white", midpoint=0) +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust=1, color = xcol, face = xbold),
          axis.text.y = element_text(color = ycol, face = ybold),
          legend.position = "none",
          axis.title = element_blank())
}


p_cormint <- make_cor(cbind(mint, metamint), qname_x = FALSE, qname_y = FALSE, textsize = 2.5,
                      xcol = "#00838F", ycol = "#00838F") +
  labs(title="Correlations between MINT dimensions") +
  theme(axis.title = element_blank())

p_cormint

Convegent Validity

Interoception

p_corintero <- make_cor(select(df, starts_with("MAIA"), IAS, BPQ),
                        cbind(mint, metamint), qname_y = FALSE, textsize = 2.5,
                        ycol = "#00838F") +
  labs(title="MINT vs. Other Interoception Questionnaires") +
  theme(axis.title = element_blank())
p_corintero

Most correlated dimensions: - IAS: ExAc - Trusting: RelA - Noticing: Resp - Emotional Awareness: Resp - Body Listening: Gast - SelfRegulation: RelA - AttentionRegulation: RelA - BPQ: Card

Alexithymia

table_models <- function(models) {
  test <- test_performance(models)
  table <- compare_performance(models, metrics=c("R2", "BIC"))
  test$Model <- NULL
  test$Omega2 <- NULL
  test$p_Omega2 <- NULL
  test$R2 <- table$R2
  test$BIC <- table$BIC
  
  preds <- c()
  coefs <- c()
  for(m in models){
    x <- names(sort(abs(coef(m)[-1]), decreasing=TRUE))
    preds <- c(preds, paste0(cleanlabels(x), collapse = ", "))
    if(length(coef(m)) == 2) {
      coefs <- c(coefs, coef(m)[2])
    }
  }
  
  if(length(coefs) == length(models)) test$Coefficient <- coefs
  format(test, zap_small = TRUE)
}

# Individual predictors ------------------------------------------------------

compare_predictors <- function(outcome="TAS_DIF", family = "linear") {
  
  preds <- c("BPQ", "IAS", 
             names(select(df, starts_with("MINT"), -matches("[[:digit:]]"))),
             names(select(df, starts_with("MAIA"))),
             names(metamint))
  
  models <- list()
  for(p in preds) {
    if(length(unique(df[[outcome]])) > 2) {
      if (family == "linear") {
        models[[p]] <- lm(as.formula(paste0(outcome, " ~ ", p)), data=cbind(df, metamint))
      } else {
        models[[p]] <- glm(as.formula(paste0(outcome, " ~ ", p)), data=cbind(df, metamint), family=family)
      }
    } else {
      models[[p]] <- glm(as.formula(paste0(outcome, " ~ ", p)), data=standardize(cbind(df, metamint), exclude=outcome), family="binomial")
    }
  }
  perf <- compare_performance(models) |> 
    arrange(AIC)
  
  best_mint <- perf$Name[str_detect(perf$Name, "MINT_")][1]
  best_maia <- perf$Name[str_detect(perf$Name, "MAIA")][1]
  
  table <- table_models(models[c(best_mint, best_maia, "BPQ", "IAS")])
  
  # Make plots --------------------------------------------------
  df_pred <- data.frame()
  for(p in c(best_mint, best_maia, "BPQ", "IAS")) {
    pred <- modelbased::estimate_relation(models[[p]], by=p, length=30) 
    names(pred)[1] <- "Value"
    pred$Predictor <- p
    df_pred <- rbind(df_pred, pred)
  }
  
  cols <- colors
  cols[[best_mint]] <- cols["Mint"]
  cols[[best_maia]] <- cols["MAIA"]
  
  p <- df_pred |>
    mutate(Predictor = fct_rev(Predictor)) |> 
    ggplot(aes(x=Value, y=Predicted)) +
    geom_ribbon(aes(ymin=CI_low, ymax=CI_high, fill=Predictor), alpha=0.3) +
    geom_line(aes(color=Predictor)) +
    scale_fill_manual(values=cols) +
    scale_color_manual(values=cols) +
    facet_wrap(~Predictor, scales="free_x") +
    labs(y=outcome, x="Interoception Index")
  
  list(table=table, p=p, outcome=outcome)
}




# Full Models ------------------------------------------------------

compare_models_lm <- function(outcome="TAS_DIF") {
  
  # Full Questionnaires
  m_mint <- lm(as.formula(paste0(outcome, " ~ .")),
              data = cbind(mint, df[outcome]))
  m_metamint <- lm(as.formula(paste0(outcome, " ~ .")),
              data = cbind(metamint, df[outcome])) 
  m_minimint <- lm(as.formula(paste0(outcome, " ~ .")),
              data = cbind(minimint, df[outcome])) 
  m_micromint <- lm(as.formula(paste0(outcome, " ~ .")),
              data = cbind(micromint, df[outcome])) 
  m_ias <- lm(as.formula(paste0(outcome, " ~ IAS")),
              data = df)
  m_maia <- lm(as.formula(paste0(outcome, " ~ .")),
                data = select(df, all_of(outcome), starts_with("MAIA"))) 
  m_imaia <- lm(as.formula(paste0(outcome, " ~ .")),
                data = select(df, all_of(outcome), 
                              starts_with("MAIA"), 
                              -contains("NotWorrying"),
                              -contains("NotDistracting"))) 
  m_imaia <- lm(as.formula(paste0(outcome, " ~ .")),
                data = select(df, all_of(outcome),
                              # MAIA_AttentionRegulation, # remove?
                              # MAIA_SelfRegulation, # remove?
                              # MAIA_Trusting, # remove?
                              MAIA_BodyListening, 
                              MAIA_EmotionalAwareness,
                              MAIA_Noticing))
  m_bpq <- lm(as.formula(paste0(outcome, " ~ BPQ")),
                data = df)
  
  models_full <- list(m_mint=m_mint, m_metamint=m_metamint,
                      m_minimint=m_minimint, m_micromint=m_micromint, 
                      m_maia=m_maia, m_imaia=m_imaia, 
                      m_ias=m_ias, m_bpq=m_bpq)
  table_models(models_full)
}

preds_dif <- compare_predictors("TAS_DIF")
display(preds_dif$table, title="DIF")

DIF

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.22	1108.37	0.31
MAIA_Trusting	< 0.001	1.33	0.091	0.17	1150.81	-0.18
BPQ	< 0.001	7.14	< .001	0.01	1286.24	0.05
IAS	< 0.001	3.81	< .001	0.08	1226.64	-0.02

Each model is compared to MINT_Deficit.

preds_dif$p

mods_dif <- compare_models_lm("TAS_DIF")
display(mods_dif, title="DIF")

DIF

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.31	1081.51
m_metamint	> 1000	2.53	0.006	0.29	1052.69
m_minimint	< 0.001	4.25	< .001	0.24	1104.97
m_micromint	< 0.001	5.41	< .001	0.19	1150.47
m_maia	< 0.001	2.74	0.003	0.23	1148.37
m_imaia	< 0.001	8.47	< .001	0.02	1292.91
m_ias	< 0.001	6.77	< .001	0.08	1226.64
m_bpq	< 0.001	8.89	< .001	0.01	1286.24

Each model is compared to m_mint.

preds_ddf <- compare_predictors("TAS_DDF")
display(preds_ddf$table, title="DDF")

DDF

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.18	1489.14	0.35
MAIA_Trusting	0.003	0.37	0.356	0.17	1500.71	-0.22
BPQ	< 0.001	5.99	< .001	0.01	1629.83	0.07
IAS	< 0.001	2.79	0.003	0.08	1571.78	-0.02

Each model is compared to MINT_Deficit.

preds_ddf$p

mods_ddf <- compare_models_lm("TAS_DDF")
display(mods_ddf, title="DDF")

DDF

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.25	1485.21
m_metamint	> 1000	1.51	0.065	0.24	1441.45
m_minimint	0.045	3.75	< .001	0.19	1491.40
m_micromint	< 0.001	4.78	< .001	0.15	1528.48
m_maia	< 0.001	1.31	0.095	0.22	1502.42
m_imaia	< 0.001	7.45	< .001	0.01	1640.19
m_ias	< 0.001	5.26	< .001	0.08	1571.78
m_bpq	< 0.001	7.69	< .001	0.01	1629.83

Each model is compared to m_mint.

preds_eot <- compare_predictors("TAS_EOT")
display(preds_eot$table, title="EOT")

EOT

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.20	931.21	0.25
MAIA_Trusting	< 0.001	3.81	< .001	0.07	1034.62	-0.10
BPQ	< 0.001	6.29	< .001	0.01	1080.86	0.07
IAS	< 0.001	4.78	< .001	0.04	1061.30	-0.01

Each model is compared to MINT_Deficit.

preds_eot$p

mods_eot <- compare_models_lm("TAS_EOT")
display(mods_eot, title="EOT")

EOT

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.24	959.67
m_metamint	> 1000	1.25	0.106	0.23	913.25
m_minimint	0.017	3.93	< .001	0.17	967.79
m_micromint	< 0.001	5.28	< .001	0.12	1011.59
m_maia	< 0.001	2.69	0.004	0.15	1017.28
m_imaia	< 0.001	6.57	< .001	0.01	1096.63
m_ias	< 0.001	6.33	< .001	0.04	1061.30
m_bpq	< 0.001	6.99	< .001	0.01	1080.86

Each model is compared to m_mint.

make_cor(select(df, starts_with("TAS_")),
         cbind(mint, metamint, select(df, starts_with("MAIA"), IAS, BPQ)))

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

Mood

preds_ls <- compare_predictors("LifeSatisfaction")
display(preds_ls$table, title="Life Satisfaction")

Life Satisfaction

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.05	3104.38	-0.51
MAIA_Trusting	> 1000	-6.57	< .001	0.28	2896.13	0.80
BPQ	< 0.001	3.09	0.001	0.00	3141.29	-0.06
IAS	0.970	0.00	0.499	0.05	3104.44	0.04

Each model is compared to MINT_Deficit.

preds_ls$p

mods_ls <- compare_models_lm("LifeSatisfaction")
display(mods_ls, title="Life Satisfaction")

Life Satisfaction

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.11	3123.86
m_metamint	> 1000	1.89	0.030	0.09	3086.66
m_minimint	> 1000	1.82	0.035	0.09	3086.07
m_micromint	> 1000	2.88	0.002	0.06	3108.06
m_maia	> 1000	-6.34	< .001	0.30	2922.14
m_imaia	0.149	3.11	< .001	0.04	3127.66
m_ias	> 1000	2.54	0.006	0.05	3104.44
m_bpq	< 0.001	4.51	< .001	0.00	3141.29

Each model is compared to m_mint.

preds_anx <- compare_predictors("PHQ4_Anxiety")
display(preds_anx$table, title="PHQ4 Anxiety")

PHQ4 Anxiety

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_CaCo				0.20	2776.91	0.52
MAIA_Trusting	75.10	-0.29	0.385	0.21	2768.28	-0.60
BPQ	< 0.001	5.58	< .001	0.03	2918.47	0.33
IAS	< 0.001	5.81	< .001	0.02	2922.86	-0.03

Each model is compared to MINT_CaCo.

preds_anx$p

mods_anx <- compare_models_lm("PHQ4_Anxiety")
display(mods_anx, title="PHQ4 Anxiety")

PHQ4 Anxiety

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.27	2771.46
m_metamint	> 1000	2.89	0.002	0.24	2750.78
m_minimint	44.43	3.42	< .001	0.23	2763.87
m_micromint	0.002	4.32	< .001	0.20	2784.46
m_maia	> 1000	-2.74	0.003	0.36	2662.72
m_imaia	< 0.001	8.10	< .001	0.00	2950.21
m_ias	< 0.001	7.89	< .001	0.02	2922.86
m_bpq	< 0.001	7.63	< .001	0.03	2918.47

Each model is compared to m_mint.

preds_dep <- compare_predictors("PHQ4_Depression")
display(preds_dep$table, title="PHQ4 Depression")

PHQ4 Depression

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.14	2631.44	0.65
MAIA_Trusting	> 1000	-2.66	0.004	0.24	2544.32	-0.55
BPQ	< 0.001	5.09	< .001	0.02	2728.02	0.24
IAS	< 0.001	4.15	< .001	0.03	2721.19	-0.02

Each model is compared to MINT_Deficit.

preds_dep$p

mods_dep <- compare_models_lm("PHQ4_Depression")
display(mods_dep, title="PHQ4 Depression")

PHQ4 Depression

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.19	2649.42
m_metamint	> 1000	2.41	0.008	0.17	2618.44
m_minimint	> 1000	2.81	0.002	0.15	2631.95
m_micromint	1.31	3.73	< .001	0.14	2648.89
m_maia	> 1000	-3.43	< .001	0.30	2523.07
m_imaia	< 0.001	6.78	< .001	0.01	2750.60
m_ias	< 0.001	6.12	< .001	0.03	2721.19
m_bpq	< 0.001	6.17	< .001	0.02	2728.02

Each model is compared to m_mint.

make_cor(select(df, LifeSatisfaction, starts_with("PHQ4_")),
         cbind(mint, metamint, select(df, starts_with("MAIA"), IAS, BPQ)))

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

• CaCo and RelA

Emotions

preds_arou <- compare_predictors("ERS_Arousal")
display(preds_arou$table, title="ERS Arousal")

ERS Arousal

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.20	2158.88	0.58
MAIA_NotWorrying	0.018	0.24	0.403	0.19	2166.87	-0.43
BPQ	< 0.001	5.40	< .001	0.04	2293.10	0.24
IAS	< 0.001	6.18	< .001	0.00	2317.10	-0.01

Each model is compared to MINT_Deficit.

preds_arou$p

mods_arou <- compare_models_lm("ERS_Arousal")
display(mods_arou, title="ERS Arousal")

ERS Arousal

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.23	2193.71
m_metamint	> 1000	2.26	0.012	0.21	2162.32
m_minimint	0.742	3.66	< .001	0.17	2194.31
m_micromint	< 0.001	4.49	< .001	0.14	2220.35
m_maia	> 1000	-2.72	0.003	0.32	2084.82
m_imaia	< 0.001	5.65	< .001	0.04	2302.61
m_ias	< 0.001	7.08	< .001	0.00	2317.10
m_bpq	< 0.001	6.41	< .001	0.04	2293.10

Each model is compared to m_mint.

preds_sens <- compare_predictors("ERS_Sensitivity")
display(preds_sens$table, title="ERS Sensitivity")

ERS Sensitivity

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.18	2322.13	0.60
MAIA_NotWorrying	> 1000	-1.16	0.122	0.22	2284.12	-0.52
BPQ	< 0.001	5.32	< .001	0.03	2442.94	0.24
IAS	< 0.001	6.03	< .001	0.00	2462.74	-0.01

Each model is compared to MINT_Deficit.

preds_sens$p

mods_sens <- compare_models_lm("ERS_Sensitivity")
display(mods_sens, title="ERS Sensitivity")

ERS Sensitivity

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.21	2354.48
m_metamint	> 1000	2.58	0.005	0.18	2327.69
m_minimint	> 1000	2.96	0.002	0.17	2336.80
m_micromint	0.010	4.12	< .001	0.14	2363.76
m_maia	> 1000	-3.02	0.001	0.30	2242.73
m_imaia	< 0.001	6.02	< .001	0.02	2461.56
m_ias	< 0.001	6.85	< .001	0.00	2462.74
m_bpq	< 0.001	6.21	< .001	0.03	2442.94

Each model is compared to m_mint.

preds_pers <- compare_predictors("ERS_Persistence")
display(preds_pers$table, title="ERS Persistence")

ERS Persistence

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.18	2230.34	0.58
MAIA_NotWorrying	> 1000	-0.49	0.314	0.20	2214.67	-0.46
BPQ	< 0.001	5.50	< .001	0.02	2360.53	0.20
IAS	< 0.001	5.16	< .001	0.02	2360.32	-0.02

Each model is compared to MINT_Deficit.

preds_pers$p

mods_pers <- compare_models_lm("ERS_Persistence")
display(mods_pers, title="ERS Persistence")

ERS Persistence

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.21	2268.66
m_metamint	> 1000	2.10	0.018	0.19	2233.85
m_minimint	392.09	3.11	< .001	0.17	2256.71
m_micromint	0.115	3.72	< .001	0.15	2272.99
m_maia	> 1000	-2.71	0.003	0.30	2162.52
m_imaia	< 0.001	5.70	< .001	0.02	2374.26
m_ias	< 0.001	6.18	< .001	0.02	2360.32
m_bpq	< 0.001	6.20	< .001	0.02	2360.53

Each model is compared to m_mint.

make_cor(select(df, starts_with("ERS_")),
         cbind(mint, metamint, select(df, starts_with("MAIA"), IAS, BPQ)))

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
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ℹ Results may be unexpected or may change in future versions of ggplot2.

• CaCo

Exploratory

CEFSA

make_cor(select(df, starts_with("MINT"), -matches("[[:digit:]]")), 
         select(df, starts_with("CEFSA")))

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
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ℹ Results may be unexpected or may change in future versions of ggplot2.

make_cor(select(df, starts_with("MAIA"), IAS, BPQ), 
         select(df, starts_with("CEFSA")))

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

preds_cefsabody <- compare_predictors("CEFSA_Body")
display(preds_cefsabody$table, title="CEFSA - Body")

CEFSA - Body

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_CaCo				0.23	1796.97	0.29
MAIA_Trusting	< 0.001	1.33	0.092	0.18	1842.88	-0.29
BPQ	< 0.001	5.60	< .001	0.03	1963.32	0.18
IAS	< 0.001	4.41	< .001	0.06	1941.95	-0.02

Each model is compared to MINT_CaCo.

preds_cefsabody$p

mods_cefsabody <- compare_models_lm("CEFSA_Body")
display(mods_cefsabody, title="CEFSA - Body")

CEFSA - Body

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.31	1775.41
m_metamint	> 1000	2.24	0.013	0.29	1746.37
m_minimint	4.55	3.36	< .001	0.27	1772.38
m_micromint	< 0.001	4.20	< .001	0.24	1799.86
m_maia	< 0.001	3.04	0.001	0.22	1849.23
m_imaia	< 0.001	8.40	< .001	0.00	1999.73
m_ias	< 0.001	7.18	< .001	0.06	1941.95
m_bpq	< 0.001	7.81	< .001	0.03	1963.32

Each model is compared to m_mint.

preds_cefsaself <- compare_predictors("CEFSA_Self")
display(preds_cefsaself$table, title="CEFSA - Self")

CEFSA - Self

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.21	2032.82	0.56
MAIA_Trusting	< 0.001	2.75	0.003	0.12	2110.11	-0.28
BPQ	< 0.001	6.87	< .001	0.02	2191.47	0.17
IAS	< 0.001	3.87	< .001	0.07	2149.73	-0.03

Each model is compared to MINT_Deficit.

preds_cefsaself$p

mods_cefsaself <- compare_models_lm("CEFSA_Self")
display(mods_cefsaself, title="CEFSA - Self")

CEFSA - Self

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.28	2034.87
m_metamint	> 1000	2.15	0.016	0.26	2003.23
m_minimint	6.92	3.46	< .001	0.23	2031.00
m_micromint	< 0.001	4.65	< .001	0.19	2069.05
m_maia	< 0.001	3.21	< .001	0.18	2106.09
m_imaia	< 0.001	8.22	< .001	0.00	2218.46
m_ias	< 0.001	6.08	< .001	0.07	2149.73
m_bpq	< 0.001	7.76	< .001	0.02	2191.47

Each model is compared to m_mint.

preds_cefsaemo <- compare_predictors("CEFSA_Emotion")
display(preds_cefsaemo$table, title="CEFSA - Emotion")

CEFSA - Emotion

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.14	2143.82	0.47
MAIA_Trusting	> 1000	-0.88	0.189	0.17	2118.54	-0.34
BPQ	< 0.001	5.23	< .001	0.01	2250.82	0.09
IAS	0.010	0.32	0.376	0.13	2153.04	-0.04

Each model is compared to MINT_Deficit.

preds_cefsaemo$p

mods_cefsaemo <- compare_models_lm("CEFSA_Emotion")
display(mods_cefsaemo, title="CEFSA - Emotion")

CEFSA - Emotion

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.27	2089.99
m_metamint	> 1000	2.17	0.015	0.25	2060.65
m_minimint	< 0.001	4.33	< .001	0.19	2112.18
m_micromint	< 0.001	5.21	< .001	0.15	2150.24
m_maia	< 0.001	2.39	0.008	0.19	2144.24
m_imaia	< 0.001	6.29	< .001	0.05	2228.72
m_ias	< 0.001	4.38	< .001	0.13	2153.04
m_bpq	< 0.001	7.78	< .001	0.01	2250.82

Each model is compared to m_mint.

preds_cefsareal <- compare_predictors("CEFSA_Reality")
display(preds_cefsareal$table, title="CEFSA - Reality")

CEFSA - Reality

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.18	1899.23	0.46
MAIA_Trusting	< 0.001	1.14	0.127	0.14	1930.10	-0.27
BPQ	< 0.001	6.63	< .001	0.01	2034.63	0.13
IAS	< 0.001	3.97	< .001	0.06	1999.65	-0.02

Each model is compared to MINT_Deficit.

preds_cefsareal$p

mods_cefsareal <- compare_models_lm("CEFSA_Reality")
display(mods_cefsareal, title="CEFSA - Reality")

CEFSA - Reality

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.24	1906.86
m_metamint	> 1000	1.69	0.046	0.23	1866.90
m_minimint	189.67	3.06	0.001	0.20	1896.37
m_micromint	< 0.001	4.24	< .001	0.16	1931.22
m_maia	< 0.001	2.30	0.011	0.18	1946.66
m_imaia	< 0.001	7.36	< .001	0.01	2053.67
m_ias	< 0.001	5.98	< .001	0.06	1999.65
m_bpq	< 0.001	7.22	< .001	0.01	2034.63

Each model is compared to m_mint.

preds_cefsacon <- compare_predictors("CEFSA_Connection")
display(preds_cefsacon$table, title="CEFSA - Connection")

CEFSA - Connection

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.17	2273.53	0.58
MAIA_Trusting	> 1000	-2.08	0.019	0.25	2201.91	-0.46
BPQ	< 0.001	5.89	< .001	0.01	2402.97	0.16
IAS	< 0.001	2.58	0.005	0.09	2346.87	-0.03

Each model is compared to MINT_Deficit.

preds_cefsacon$p

mods_cefsacon <- compare_models_lm("CEFSA_Connection")
display(mods_cefsacon, title="CEFSA - Connection")

CEFSA - Connection

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.25	2263.13
m_metamint	> 1000	2.38	0.009	0.23	2235.43
m_minimint	21.99	3.43	< .001	0.21	2256.94
m_micromint	< 0.001	4.52	< .001	0.17	2292.99
m_maia	> 1000	-0.73	0.233	0.28	2220.93
m_imaia	< 0.001	7.32	< .001	0.02	2409.99
m_ias	< 0.001	5.31	< .001	0.09	2346.87
m_bpq	< 0.001	7.55	< .001	0.01	2402.97

Each model is compared to m_mint.

preds_cefsagency <- compare_predictors("CEFSA_Agency")
display(preds_cefsagency$table, title="CEFSA - Agency")

CEFSA - Agency

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.18	2122.91	0.54
MAIA_Trusting	< 0.001	1.07	0.142	0.15	2153.22	-0.32
BPQ	< 0.001	6.30	< .001	0.02	2260.65	0.15
IAS	< 0.001	3.97	< .001	0.06	2227.64	-0.03

Each model is compared to MINT_Deficit.

preds_cefsagency$p

mods_cefsagency <- compare_models_lm("CEFSA_Agency")
display(mods_cefsagency, title="CEFSA - Agency")

CEFSA - Agency

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.25	2124.18
m_metamint	> 1000	2.21	0.014	0.23	2092.38
m_minimint	< 0.001	4.14	< .001	0.18	2141.94
m_micromint	< 0.001	4.80	< .001	0.15	2163.94
m_maia	< 0.001	2.14	0.016	0.19	2164.45
m_imaia	< 0.001	7.77	< .001	0.01	2279.55
m_ias	< 0.001	6.23	< .001	0.06	2227.64
m_bpq	< 0.001	7.44	< .001	0.02	2260.65

Each model is compared to m_mint.

Emotion Regulation

make_cor(select(df, starts_with("MINT"), -matches("[[:digit:]]")), 
         select(df, starts_with("CERQ")))

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

make_cor(select(df, starts_with("MAIA"), IAS, BPQ), 
         select(df, starts_with("CERQ")))

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

preds_cerqreapp <- compare_predictors("CERQ_PositiveReappraisal")
display(preds_cerqreapp$table, title="CERQ - Positive Reappraisal")

CERQ - Positive Reappraisal

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Awareness				0.07	1985.50	0.31
MAIA_Trusting	> 1000	-3.13	< .001	0.15	1912.14	0.28
BPQ	< 0.001	2.12	0.017	0.02	2020.91	0.15
IAS	0.332	0.17	0.431	0.06	1987.71	0.02

Each model is compared to MINT_Awareness.

preds_cerqreapp$p

mods_cerqreapp <- compare_models_lm("CERQ_PositiveReappraisal")
display(mods_cerqreapp, title="CERQ - Positive Reappraisal")

CERQ - Positive Reappraisal

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.10	2024.82
m_metamint	> 1000	2.23	0.013	0.07	1994.86
m_minimint	> 1000	2.76	0.003	0.05	2007.83
m_micromint	44.67	3.16	< .001	0.04	2017.22
m_maia	> 1000	-3.61	< .001	0.21	1908.44
m_imaia	> 1000	-0.55	0.293	0.11	1960.87
m_ias	> 1000	1.67	0.048	0.06	1987.71
m_bpq	7.06	3.30	< .001	0.02	2020.91

Each model is compared to m_mint.

preds_cerqrefocus <- compare_predictors("CERQ_PositiveRefocusing")
display(preds_cerqrefocus$table, title="CERQ - Positive Refocusing")

CERQ - Positive Refocusing

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_RelA				0.06	2108.34	0.21
MAIA_SelfRegulation	> 1000	-2.90	0.002	0.14	2045.49	0.29
BPQ	< 0.001	2.99	0.001	0.00	2152.33	0.05
IAS	< 0.001	2.07	0.019	0.02	2135.43	0.02

Each model is compared to MINT_RelA.

preds_cerqrefocus$p

mods_cerqrefocus <- compare_models_lm("CERQ_PositiveRefocusing")
display(mods_cerqrefocus, title="CERQ - Positive Refocusing")

CERQ - Positive Refocusing

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.07	2163.05
m_metamint	> 1000	2.45	0.007	0.04	2136.82
m_minimint	> 1000	2.28	0.011	0.04	2133.84
m_micromint	> 1000	2.77	0.003	0.03	2147.23
m_maia	> 1000	-4.17	< .001	0.19	2043.21
m_imaia	> 1000	-0.94	0.172	0.10	2092.09
m_ias	> 1000	2.46	0.007	0.02	2135.43
m_bpq	213.01	3.31	< .001	0.00	2152.33

Each model is compared to m_mint.

preds_cerqpersp <- compare_predictors("CERQ_Perspective")
display(preds_cerqpersp$table, title="CERQ - Perspective")

CERQ - Perspective

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Awareness				0.04	1939.81	0.24
MAIA_AttentionRegulation	> 1000	-1.81	0.035	0.08	1907.97	0.24
BPQ	< 0.001	2.23	0.013	0.01	1966.83	0.09
IAS	0.119	0.43	0.333	0.04	1944.07	0.02

Each model is compared to MINT_Awareness.

preds_cerqpersp$p

mods_cerqpersp <- compare_models_lm("CERQ_Perspective")
display(mods_cerqpersp, title="CERQ - Perspective")

CERQ - Perspective

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.07	1988.72
m_metamint	> 1000	1.68	0.046	0.05	1951.29
m_minimint	> 1000	2.05	0.020	0.04	1959.08
m_micromint	> 1000	2.42	0.008	0.03	1966.07
m_maia	> 1000	-3.06	0.001	0.16	1892.96
m_imaia	> 1000	1.97	0.024	0.03	1964.20
m_ias	> 1000	1.43	0.076	0.04	1944.07
m_bpq	> 1000	2.97	0.001	0.01	1966.83

Each model is compared to m_mint.

preds_cerqacceptance <- compare_predictors("CERQ_Acceptance")
display(preds_cerqacceptance$table, title="CERQ - Acceptance")

CERQ - Acceptance

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Awareness				0.02	1870.51	0.14
MAIA_NotDistracting	5.87	-0.31	0.379	0.02	1866.98	-0.13
BPQ	0.027	0.88	0.190	0.01	1877.74	0.09
IAS	0.339	0.36	0.358	0.02	1872.68	0.01

Each model is compared to MINT_Awareness.

preds_cerqacceptance$p

mods_cerqacceptance <- compare_models_lm("CERQ_Acceptance")
display(mods_cerqacceptance, title="CERQ - Acceptance")

CERQ - Acceptance

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.03	1923.83
m_metamint	> 1000	1.39	0.082	0.02	1879.76
m_minimint	> 1000	1.68	0.047	0.02	1883.76
m_micromint	> 1000	1.92	0.027	0.01	1886.77
m_maia	> 1000	-0.82	0.205	0.05	1892.94
m_imaia	> 1000	0.81	0.209	0.02	1879.39
m_ias	> 1000	1.48	0.069	0.02	1872.68
m_bpq	> 1000	1.92	0.028	0.01	1877.74

Each model is compared to m_mint.

Primals

make_cor(select(df, starts_with("MINT"), -matches("[[:digit:]]")), 
         select(df, starts_with("PI")))

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

make_cor(select(df, starts_with("MAIA"), IAS, BPQ), 
         select(df, starts_with("PI")))

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

preds_pialive <- compare_predictors("PI_Alive")
display(preds_pialive$table, title="Primals - Alive")

Primals - Alive

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Visceroception				0.07	2374.58	0.28
MAIA_EmotionalAwareness	> 1000	-2.10	0.018	0.13	2331.48	0.39
BPQ	< 0.001	3.24	< .001	0.02	2418.47	0.17
IAS	0.006	0.57	0.285	0.06	2384.87	0.03

Each model is compared to MINT_Visceroception.

preds_pialive$p

mods_pialive <- compare_models_lm("PI_Alive")
display(mods_pialive, title="Primals - Alive")

Primals - Alive

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.13	2394.67
m_metamint	> 1000	2.65	0.004	0.09	2369.95
m_minimint	> 1000	2.32	0.010	0.10	2363.89
m_micromint	163.03	3.22	< .001	0.08	2384.48
m_maia	> 1000	-1.60	0.055	0.17	2337.51
m_imaia	> 1000	-0.74	0.229	0.15	2325.77
m_ias	134.32	2.99	0.001	0.06	2384.87
m_bpq	< 0.001	4.91	< .001	0.02	2418.47

Each model is compared to m_mint.

preds_pisafe <- compare_predictors("PI_Safe")
display(preds_pisafe$table, title="Primals - Safe")

Primals - Safe

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.12	1775.84	-0.34
MAIA_Trusting	> 1000	-0.96	0.168	0.15	1750.87	0.25
BPQ	< 0.001	3.15	< .001	0.05	1836.76	-0.20
IAS	< 0.001	3.17	< .001	0.04	1842.84	0.02

Each model is compared to MINT_Deficit.

preds_pisafe$p

mods_pisafe <- compare_models_lm("PI_Safe")
display(mods_pisafe, title="Primals - Safe")

Primals - Safe

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.18	1794.07
m_metamint	> 1000	2.32	0.010	0.15	1763.83
m_minimint	1.91	3.62	< .001	0.12	1792.78
m_micromint	< 0.001	4.52	< .001	0.09	1819.00
m_maia	> 1000	-1.54	0.062	0.23	1728.15
m_imaia	< 0.001	6.14	< .001	0.00	1883.38
m_ias	< 0.001	4.90	< .001	0.04	1842.84
m_bpq	< 0.001	4.46	< .001	0.05	1836.76

Each model is compared to m_mint.

preds_pient <- compare_predictors("PI_Enticing")
display(preds_pient$table, title="Primals - Enticing")

Primals - Enticing

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Awareness				0.07	1725.24	0.26
MAIA_Trusting	> 1000	-2.86	0.002	0.14	1663.95	0.22
BPQ	< 0.001	3.40	< .001	0.00	1776.61	-0.01
IAS	0.391	0.12	0.454	0.06	1727.12	0.02

Each model is compared to MINT_Awareness.

preds_pient$p

mods_pient <- compare_models_lm("PI_Enticing")
display(mods_pient, title="Primals - Enticing")

Primals - Enticing

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.11	1753.01
m_metamint	> 1000	1.59	0.056	0.10	1710.93
m_minimint	395.91	3.25	< .001	0.06	1741.05
m_micromint	1.92	3.51	< .001	0.05	1751.70
m_maia	> 1000	-1.93	0.027	0.17	1685.31
m_imaia	0.017	3.14	< .001	0.04	1761.18
m_ias	> 1000	2.04	0.021	0.06	1727.12
m_bpq	< 0.001	4.76	< .001	0.00	1776.61

Each model is compared to m_mint.

preds_pigood <- compare_predictors("PI_Good")
display(preds_pigood$table, title="Primals - Good")

Primals - Good

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.10	1526.10	-0.26
MAIA_Trusting	> 1000	-2.94	0.002	0.20	1444.02	0.23
BPQ	< 0.001	3.87	< .001	0.02	1593.21	-0.10
IAS	< 0.001	1.27	0.102	0.07	1554.19	0.02

Each model is compared to MINT_Deficit.

preds_pigood$p

mods_pigood <- compare_models_lm("PI_Good")
display(mods_pigood, title="Primals - Good")

Primals - Good

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.17	1530.74
m_metamint	> 1000	1.77	0.038	0.16	1490.62
m_minimint	2.92	3.69	< .001	0.12	1528.60
m_micromint	< 0.001	4.29	< .001	0.09	1553.14
m_maia	> 1000	-2.28	0.011	0.25	1438.30
m_imaia	< 0.001	5.54	< .001	0.02	1608.05
m_ias	< 0.001	3.65	< .001	0.07	1554.19
m_bpq	< 0.001	5.22	< .001	0.02	1593.21

Each model is compared to m_mint.

Predictive

Mental Health

Mood Disorders

compare_models_glm <- function(outcome="Disorders_Psychiatric_Mood", title="", subtitle = "") {
  
  # Full Questionnaires
  m_mint <- glm(as.formula(paste0(outcome, " ~ .")),
              data = cbind(mint, df[outcome]),
              family = "binomial")
  m_metamint <- glm(as.formula(paste0(outcome, " ~ .")),
              data = cbind(metamint, df[outcome]),
              family = "binomial")
  m_minimint <- glm(as.formula(paste0(outcome, " ~ .")),
              data = cbind(minimint, df[outcome]),
              family = "binomial") 
  m_micromint <- glm(as.formula(paste0(outcome, " ~ .")),
              data = cbind(micromint, df[outcome]),
              family = "binomial") 
  m_ias <- glm(as.formula(paste0(outcome, " ~ IAS")),
              data = df,
              family = "binomial")
  m_maia <- glm(as.formula(paste0(outcome, " ~ .")),
                data = select(df, all_of(outcome), starts_with("MAIA")),
              family = "binomial") 
  m_imaia <- glm(as.formula(paste0(outcome, " ~ .")),
                data = select(df, all_of(outcome), 
                              starts_with("MAIA"), 
                              -contains("NotWorrying"),
                              -contains("NotDistracting")),
              family = "binomial")
  m_imaia <- glm(as.formula(paste0(outcome, " ~ .")),
                data = select(df, all_of(outcome),
                              # MAIA_AttentionRegulation, # remove?
                              # MAIA_SelfRegulation, # remove?
                              # MAIA_Trusting, # remove?
                              MAIA_BodyListening, 
                              MAIA_EmotionalAwareness,
                              MAIA_Noticing),
              family = "binomial")
  m_bpq <- glm(as.formula(paste0(outcome, " ~ BPQ")),
               data = df,
               family = "binomial")
  
  models_full <- list(m_mint=m_mint, m_metamint=m_metamint, 
                      m_minimint=m_minimint, m_micromint=m_micromint,
                      m_maia=m_maia, m_imaia=m_imaia, 
                      m_ias=m_ias, m_bpq=m_bpq)
  
  table <- table_models(models_full)

  get_auc <- function(m) {
    roc <- as.data.frame(performance_roc(m))
    auc <- bayestestR::area_under_curve(roc$Specificity, roc$Sensitivity)
    paste0("AUC = ", insight::format_percent(auc))
  }
  
  mods_order <- c("Mint", "metaMint", "miniMint", "microMint", "MAIA", "iMAIA", "IAS", "BPQ")

  p <- rbind(
    mutate(as.data.frame(performance_roc(m_mint)), Predictor = "Mint"),
    mutate(as.data.frame(performance_roc(m_metamint)), Predictor = "metaMint"),
    mutate(as.data.frame(performance_roc(m_minimint)), Predictor = "miniMint"),
    mutate(as.data.frame(performance_roc(m_micromint)), Predictor = "microMint"),
    mutate(as.data.frame(performance_roc(m_maia)), Predictor = "MAIA"),
    mutate(as.data.frame(performance_roc(m_imaia)), Predictor = "iMAIA"),
    mutate(as.data.frame(performance_roc(m_ias)), Predictor = "IAS"),
    mutate(as.data.frame(performance_roc(m_bpq)), Predictor = "BPQ")
  ) |>
    mutate(Predictor = fct_relevel(Predictor, mods_order)) |>
    ggplot(aes(x=Specificity)) +
    geom_abline(intercept=0, slope=1, color="gray", linewidth=1) +
    geom_line(aes(y=Sensitivity, color=Predictor), linewidth=1.5, alpha=0.9) +
    geom_label(data=data.frame(
      label = c(get_auc(m_mint), get_auc(m_metamint), 
                get_auc(m_minimint), get_auc(m_micromint), 
                get_auc(m_maia), get_auc(m_imaia), get_auc(m_ias), get_auc(m_bpq)),
      Predictor = mods_order,
      x = 0.8,
      y = c(0.55, 0.475, 0.425, 0.375, 0.30, 0.25, 0.20, 0.15)),
      aes(x=x, y=y, label=label), color=colors[mods_order], size=3) +
    labs(x= "1 - Specificity (False Positive Rate)", y="Sensitivity (True Positive Rate)",
         color="Questionnaire", title = title, subtitle=subtitle) +
    scale_color_manual(values=colors) +
    scale_x_continuous(labels=scales::percent) +
    scale_y_continuous(labels=scales::percent) +
    theme_minimal() +
    theme(plot.title = element_text(face="bold"))
  
  list(table=table, p=p)
}

preds_mood <- compare_predictors("Disorders_Psychiatric_Mood")
display(preds_mood$table, title="Mood Disorders")

Mood Disorders

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.07	647.74	0.75
MAIA_Trusting	> 1000	-1.10	0.136	0.10	628.01	-0.84
BPQ	< 0.001	3.41	< .001	0.01	693.45	0.25
IAS	< 0.001	3.45	< .001	0.00	697.06	-0.16

Each model is compared to MINT_Deficit.

preds_mood$p

mods_mood <- compare_models_glm("Disorders_Psychiatric_Mood")
display(mods_mood$table, title="Mood Disorders")

Mood Disorders

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.11	693.59
m_metamint	> 1000	1.57	0.058	0.09	651.91
m_minimint	> 1000	2.29	0.011	0.08	660.53
m_micromint	> 1000	2.29	0.011	0.07	664.76
m_maia	> 1000	-2.67	0.004	0.18	624.14
m_imaia	< 0.001	4.13	< .001	0.00	710.98
m_ias	0.176	4.09	< .001	0.00	697.06
m_bpq	1.07	4.11	< .001	0.01	693.45

Each model is compared to m_mint.

mods_mood$p

preds_moodt <- compare_predictors("Disorders_Psychiatric_MoodTreatment")
display(preds_moodt$table, title="Mood Disorders (with Treatment)")

Mood Disorders (with Treatment)

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.06	452.33	0.84
MAIA_Trusting	387.73	-0.81	0.210	0.08	440.41	-0.90
BPQ	< 0.001	3.07	0.001	0.00	489.91	0.24
IAS	< 0.001	2.76	0.003	0.01	488.61	-0.27

Each model is compared to MINT_Deficit.

preds_moodt$p

mods_moodt <- compare_models_glm("Disorders_Psychiatric_MoodTreatment",
                                 title="Mood Disorders", subtitle = "MDD, GAD, Bipolar (with Treatment)")
display(mods_moodt$table, title="Mood Disorders (with Treatment)")

Mood Disorders (with Treatment)

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.09	501.95
m_metamint	> 1000	1.46	0.072	0.08	456.71
m_minimint	> 1000	2.58	0.005	0.06	469.91
m_micromint	> 1000	2.21	0.014	0.06	469.83
m_maia	> 1000	-2.29	0.011	0.17	445.54
m_imaia	0.243	3.61	< .001	0.00	504.78
m_ias	790.65	3.59	< .001	0.01	488.61
m_bpq	412.07	3.65	< .001	0.00	489.91

Each model is compared to m_mint.

mods_moodt$p

Anxiety

preds_anxdis <- compare_predictors("Disorders_Psychiatric_Anxiety")
display(preds_anxdis$table, title="Anxiety Disorders")

Anxiety Disorders

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_CaCo				0.05	686.43	0.58
MAIA_Trusting	> 1000	-1.68	0.047	0.09	658.48	-0.78
BPQ	< 0.001	2.63	0.004	0.01	716.72	0.26
IAS	< 0.001	3.05	0.001	0.00	722.92	-0.10

Each model is compared to MINT_CaCo.

preds_anxdis$p

mods_anxdis <- compare_models_glm("Disorders_Psychiatric_Anxiety")
display(mods_anxdis$table, title="Anxiety Disorders")

Anxiety Disorders

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.09	729.31
m_metamint	> 1000	1.87	0.030	0.06	693.26
m_minimint	> 1000	1.98	0.024	0.06	692.87
m_micromint	> 1000	2.22	0.013	0.05	699.61
m_maia	> 1000	-3.45	< .001	0.19	646.25
m_imaia	0.068	3.74	< .001	0.00	734.68
m_ias	24.33	3.86	< .001	0.00	722.92
m_bpq	540.19	3.64	< .001	0.01	716.72

Each model is compared to m_mint.

mods_anxdis$p

preds_anxdist <- compare_predictors("Disorders_Psychiatric_AnxietyTreatment")
display(preds_anxdist$table, title="Anxiety Disorders (with Treatment)")

Anxiety Disorders (with Treatment)

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.03	398.02	0.58
MAIA_Trusting	237.81	-0.98	0.163	0.05	387.08	-0.71
BPQ	0.002	1.49	0.068	0.01	410.01	0.30
IAS	< 0.001	1.78	0.038	0.00	413.57	-0.13

Each model is compared to MINT_Deficit.

preds_anxdist$p

mods_anxdist <- compare_models_glm("Disorders_Psychiatric_AnxietyTreatment")
display(mods_anxdist$table, title="Anxiety Disorders (with Treatment)")

Anxiety Disorders (with Treatment)

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.04	457.93
m_metamint	> 1000	0.85	0.197	0.03	409.34
m_minimint	> 1000	1.32	0.094	0.02	412.24
m_micromint	> 1000	1.43	0.076	0.02	415.54
m_maia	> 1000	-2.62	0.004	0.13	400.96
m_imaia	> 1000	1.71	0.043	0.01	422.88
m_ias	> 1000	1.91	0.028	0.00	413.57
m_bpq	> 1000	1.79	0.037	0.01	410.01

Each model is compared to m_mint.

mods_anxdist$p

Eating

preds_eat <- compare_predictors("Disorders_Psychiatric_Eating")
display(preds_eat$table, title="Eating Disorders")

Eating Disorders

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_CaCo				0.03	166.00	0.96
MAIA_Trusting	480.64	-1.21	0.113	0.06	153.65	-1.29
BPQ	< 0.001	1.96	0.025	0.00	182.40	0.00
IAS	0.006	1.06	0.145	0.01	176.17	-0.58

Each model is compared to MINT_CaCo.

preds_eat$p

mods_eat <- compare_models_glm("Disorders_Psychiatric_Eating")
display(mods_eat$table, title="Eating Disorders")

Eating Disorders

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.07	217.25
m_metamint	> 1000	0.63	0.265	0.06	167.68
m_minimint	> 1000	1.26	0.104	0.06	169.36
m_micromint	> 1000	1.83	0.034	0.04	173.90
m_maia	> 1000	-0.41	0.340	0.10	192.60
m_imaia	> 1000	2.42	0.008	0.00	193.17
m_ias	> 1000	2.25	0.012	0.01	176.17
m_bpq	> 1000	2.57	0.005	0.00	182.40

Each model is compared to m_mint.

mods_eat$p

Addiction

preds_addict <- compare_predictors("Disorders_Psychiatric_Addiction")
display(preds_addict$table, title="Addiction")

Addiction

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_SexS				0.01	207.84	0.45
MAIA_Noticing	0.575	0.30	0.381	0.00	208.95	0.32
BPQ	0.379	0.41	0.340	0.00	209.78	0.25
IAS	0.244	0.70	0.242	0.00	210.66	0.14

Each model is compared to MINT_SexS.

preds_addict$p

mods_addict <- compare_models_glm("Disorders_Psychiatric_Addiction")
display(mods_addict$table, title="Addiction")

Addiction

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.01	269.86
m_metamint	> 1000	0.82	0.206	0.00	220.52
m_minimint	> 1000	0.94	0.174	0.00	222.45
m_micromint	> 1000	0.93	0.176	0.00	221.82
m_maia	> 1000	-0.22	0.414	0.01	248.58
m_imaia	> 1000	0.87	0.191	0.00	221.76
m_ias	> 1000	1.23	0.109	0.00	210.66
m_bpq	> 1000	1.01	0.157	0.00	209.78

Each model is compared to m_mint.

mods_addict$p

ADHD

preds_adhd <- compare_predictors("Disorders_Psychiatric_ADHD")
display(preds_adhd$table, title="ADHD")

ADHD

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.03	432.89	0.60
MAIA_Trusting	0.007	1.12	0.132	0.01	442.91	-0.40
BPQ	< 0.001	2.13	0.017	0.00	452.19	0.11
IAS	0.040	0.55	0.290	0.02	439.30	-0.48

Each model is compared to MINT_Deficit.

preds_adhd$p

mods_adhd <- compare_models_glm("Disorders_Psychiatric_ADHD",
                                title = "ADHD", subtitle = "Attention Deficit/Hyperactivity Disorder")
display(mods_adhd$table, title="ADHD")

ADHD

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.07	478.24
m_metamint	> 1000	1.58	0.057	0.05	434.60
m_minimint	> 1000	1.62	0.053	0.05	438.84
m_micromint	> 1000	2.24	0.012	0.02	450.87
m_maia	0.244	1.80	0.036	0.03	481.06
m_imaia	905.92	2.66	0.004	0.00	464.63
m_ias	> 1000	2.14	0.016	0.02	439.30
m_bpq	> 1000	2.75	0.003	0.00	452.19

Each model is compared to m_mint.

mods_adhd$p

Autism

preds_asd <- compare_predictors("Disorders_Psychiatric_Autism")
display(preds_asd$table, title="Autism")

Autism

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.03	367.12	0.71
MAIA_Trusting	0.076	0.58	0.280	0.02	372.28	-0.58
BPQ	< 0.001	2.36	0.009	0.00	388.27	0.14
IAS	0.005	0.96	0.170	0.02	377.64	-0.48

Each model is compared to MINT_Deficit.

preds_asd$p

mods_asd <- compare_models_glm("Disorders_Psychiatric_Autism",
                                title = "ASD", subtitle = "Autism Spectrum Disorder")
display(mods_asd$table, title="Autism")

Autism

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.08	410.51
m_metamint	> 1000	1.39	0.082	0.06	367.41
m_minimint	> 1000	1.89	0.029	0.04	376.40
m_micromint	> 1000	2.51	0.006	0.02	389.04
m_maia	0.168	1.76	0.039	0.03	414.07
m_imaia	139.07	2.96	0.002	0.00	400.64
m_ias	> 1000	2.35	0.009	0.02	377.64
m_bpq	> 1000	2.97	0.002	0.00	388.27

Each model is compared to m_mint.

mods_asd$p

Borderline

preds_bpd <- compare_predictors("Disorders_Psychiatric_Borderline")
display(preds_bpd$table, title="Borderline Personality Disorder")

Borderline Personality Disorder

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Derm				0.01	74.62	1.59
MAIA_EmotionalAwareness	6.78	-0.67	0.251	0.02	70.79	1.93
BPQ	0.647	0.13	0.446	0.01	75.49	1.31
IAS	0.018	1.45	0.073	0.00	82.71	0.17

Each model is compared to MINT_Derm.

preds_bpd$p

mods_bpd <- compare_models_glm("Disorders_Psychiatric_Borderline")
display(mods_bpd$table, title="Borderline Personality Disorder")

Borderline Personality Disorder

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.18	120.21
m_metamint	> 1000	1.79	0.037	0.02	85.38
m_minimint	> 1000	1.56	0.060	0.03	85.37
m_micromint	> 1000	1.37	0.085	0.04	85.38
m_maia	> 1000	-0.89	0.188	0.38	87.65
m_imaia	> 1000	0.97	0.166	0.07	78.69
m_ias	> 1000	1.96	0.025	0.00	82.71
m_bpq	> 1000	1.62	0.053	0.01	75.49

Each model is compared to m_mint.

mods_bpd$p

Somatic Issues

Afferent Sensitivity

preds_afferent <- compare_predictors("Disorders_Somatic_AfferentSensitivity")
display(preds_afferent$table, title="Afferent Sensitivity Symptoms")

Afferent Sensitivity Symptoms

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.04	738.45	0.50
MAIA_Trusting	0.003	1.07	0.143	0.02	750.22	-0.36
BPQ	< 0.001	2.54	0.006	0.00	763.36	0.15
IAS	< 0.001	2.52	0.006	0.00	764.33	-0.12

Each model is compared to MINT_Deficit.

preds_afferent$p

mods_afferent <- compare_models_glm("Disorders_Somatic_AfferentSensitivity",
                                    title="Afferent Sensitivity Symptoms",
                                    subtitle = "Migraine, neuropathy, muscle tension, dizziness, ...")
display(mods_afferent$table, title="Afferent Sensitivity Symptoms")

Afferent Sensitivity Symptoms

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.06	787.90
m_metamint	> 1000	1.85	0.032	0.04	749.77
m_minimint	> 1000	1.93	0.027	0.04	750.39
m_micromint	> 1000	2.49	0.006	0.03	759.68
m_maia	43.07	0.96	0.170	0.05	780.38
m_imaia	75.75	3.50	< .001	0.00	779.25
m_ias	> 1000	3.44	< .001	0.00	764.33
m_bpq	> 1000	3.40	< .001	0.00	763.36

Each model is compared to m_mint.

mods_afferent$p

Central Sensitization

preds_central <- compare_predictors("Disorders_Somatic_CentralSensitization")
display(preds_central$table, title="Central Sensitization Symptoms")

Central Sensitization Symptoms

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.03	860.88	0.38
MAIA_Trusting	0.607	0.09	0.464	0.03	861.87	-0.36
BPQ	< 0.001	2.09	0.018	0.00	879.09	0.11
IAS	< 0.001	2.06	0.020	0.00	880.18	-0.06

Each model is compared to MINT_Deficit.

preds_central$p

mods_central <- compare_models_glm("Disorders_Somatic_CentralSensitization",
                                   title="Central Sensitization Symptoms",
                                   subtitle = "Fibromyalgia, chronic fatigue, back pain, IBS, ...")
display(mods_central$table, title="Central Sensitization Symptoms")

Central Sensitization Symptoms

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.05	908.13
m_metamint	> 1000	1.88	0.030	0.03	870.25
m_minimint	> 1000	2.19	0.014	0.03	875.21
m_micromint	> 1000	2.63	0.004	0.01	884.89
m_maia	> 1000	-0.57	0.285	0.06	880.20
m_imaia	> 1000	2.66	0.004	0.00	890.41
m_ias	> 1000	3.04	0.001	0.00	880.18
m_bpq	> 1000	2.99	0.001	0.00	879.09

Each model is compared to m_mint.

mods_central$p

Autonomic Dysfunction

preds_autonomic <- compare_predictors("Disorders_Somatic_AutonomicDysfunction")
display(preds_autonomic$table, title="Autonomic Dysfunction")

Autonomic Dysfunction

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Card				0.03	698.65	0.46
MAIA_Trusting	< 0.001	1.75	0.040	0.01	716.70	-0.20
BPQ	< 0.001	2.13	0.017	0.00	717.74	0.17
IAS	< 0.001	2.30	0.011	0.00	720.99	-0.02

Each model is compared to MINT_Card.

preds_autonomic$p

mods_autonomic <- compare_models_glm("Disorders_Somatic_AutonomicDysfunction",
                                     title="Autonomic Dysfunction",
                                     subtitle = "Hypermobility, chest pain, hypo/hypertension, ....")
display(mods_autonomic$table, title="Autonomic Dysfunction")

Autonomic Dysfunction

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.07	736.12
m_metamint	494.59	3.07	0.001	0.01	723.71
m_minimint	> 1000	2.57	0.005	0.03	711.21
m_micromint	> 1000	2.99	0.001	0.02	720.48
m_maia	0.003	1.93	0.027	0.03	748.02
m_imaia	10.85	3.29	< .001	0.00	731.35
m_ias	> 1000	3.49	< .001	0.00	720.99
m_bpq	> 1000	3.31	< .001	0.00	717.74

Each model is compared to m_mint.

mods_autonomic$p

Immune-Inflammatory

preds_immune <- compare_predictors("Disorders_Somatic_ImmuneInflammatory")
display(preds_immune$table, title="Immune-Inflammatory Symptoms")

Immune-Inflammatory Symptoms

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Derm				0.07	891.57	0.60
MAIA_Trusting	< 0.001	1.86	0.032	0.03	921.09	-0.36
BPQ	< 0.001	2.87	0.002	0.01	931.00	0.26
IAS	< 0.001	3.38	< .001	0.00	940.64	-0.09

Each model is compared to MINT_Derm.

preds_immune$p

mods_immune <- compare_models_glm("Disorders_Somatic_ImmuneInflammatory",
                                  title="Immune-Inflammatory Symptoms",
                                  subtitle = "Allergies, asthma, eczema, autoimmune, ...")
display(mods_immune$table, title="Immune-Inflammatory Symptoms")

Immune-Inflammatory Symptoms

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.10	934.15
m_metamint	> 1000	3.07	0.001	0.05	919.15
m_minimint	33.21	3.43	< .001	0.04	927.14
m_micromint	4.52	3.56	< .001	0.03	931.13
m_maia	0.018	1.66	0.049	0.06	942.14
m_imaia	< 0.001	4.16	< .001	0.00	951.87
m_ias	0.039	4.36	< .001	0.00	940.64
m_bpq	4.84	3.82	< .001	0.01	931.00

Each model is compared to m_mint.

mods_immune$p

Lifestyle and Demographic

Age

preds_age <- compare_predictors("Age")
display(preds_age$table, title="Age")

Age

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Deficit				0.08	6033.91	-4.78
MAIA_NotWorrying	< 0.001	1.69	0.046	0.04	6061.48	2.74
BPQ	< 0.001	3.61	< .001	0.01	6084.98	-1.93
IAS	< 0.001	1.64	0.050	0.04	6062.47	0.29

Each model is compared to MINT_Deficit.

preds_age$p

mods_age <- compare_models_lm("Age")
display(mods_age, title="Age")

Age

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.17	6024.63
m_metamint	29.55	3.38	< .001	0.12	6017.86
m_minimint	< 0.001	4.60	< .001	0.07	6051.36
m_micromint	< 0.001	4.62	< .001	0.07	6050.92
m_maia	< 0.001	3.01	0.001	0.09	6074.60
m_imaia	< 0.001	6.34	< .001	0.00	6106.72
m_ias	< 0.001	4.87	< .001	0.04	6062.47
m_bpq	< 0.001	6.01	< .001	0.01	6084.98

Each model is compared to m_mint.

Sex

df$Sex <- ifelse(df$Gender == "Female", 1, ifelse(df$Gender == "Male", 0, NA))

preds_sex <- compare_predictors("Sex")
display(preds_sex$table, title="Sex")

Sex

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_CaCo				0.06	1004.87	0.08
MAIA_NotWorrying	0.006	0.67	0.252	0.05	1015.26	-0.10
BPQ	< 0.001	3.44	< .001	0.00	1050.06	0.03
IAS	< 0.001	3.53	< .001	0.00	1052.23	0.00

Each model is compared to MINT_CaCo.

preds_sex$p

mods_sex <- compare_models_glm("Sex")
display(mods_sex$table, title="Sex")

Sex

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.16	944.58
m_metamint	37.74	3.43	< .001	0.10	937.32
m_minimint	0.008	4.01	< .001	0.08	954.29
m_micromint	0.349	3.76	< .001	0.09	946.68
m_maia	< 0.001	2.59	0.005	0.09	980.74
m_imaia	< 0.001	5.47	< .001	0.01	1007.96
m_ias	< 0.001	5.91	< .001	0.00	998.49
m_bpq	< 0.001	5.80	< .001	0.00	996.33

Each model is compared to m_mint.

mods_sex$p

BMI

preds_bmi <- compare_predictors("BMI")
display(preds_bmi$table, title="BMI")

BMI

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Sati				0.03	4873.64	-0.89
MAIA_NotWorrying	< 0.001	1.75	0.040	0.01	4890.73	0.44
BPQ	< 0.001	2.09	0.018	0.00	4894.04	-0.17
IAS	< 0.001	1.59	0.056	0.01	4889.49	0.06

Each model is compared to MINT_Sati.

preds_bmi$p

mods_bmi <- compare_models_lm("BMI")
display(mods_bmi, title="BMI")

BMI

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.05	4922.00
m_metamint	> 1000	2.34	0.010	0.02	4894.07
m_minimint	> 1000	2.52	0.006	0.01	4899.62
m_micromint	> 1000	2.30	0.011	0.02	4893.35
m_maia	0.002	2.40	0.008	0.01	4934.85
m_imaia	> 1000	2.78	0.003	0.00	4906.31
m_ias	> 1000	2.60	0.005	0.01	4889.49
m_bpq	> 1000	2.90	0.002	0.00	4894.04

Each model is compared to m_mint.

Physical Activity

preds_physactiv <- compare_predictors("Physical_Active")
display(preds_physactiv$table, title="Physical Activity")

Physical Activity

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_RelA				0.05	2911.58	0.32
MAIA_Trusting	> 1000	-3.70	< .001	0.13	2844.29	0.47
BPQ	< 0.001	2.89	0.002	0.00	2946.74	0.01
IAS	< 0.001	1.96	0.025	0.02	2932.91	0.02

Each model is compared to MINT_RelA.

preds_physactiv$p

mods_physactiv <- compare_models_lm("Physical_Active")
display(mods_physactiv, title="Physical Activity")

Physical Activity

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.09	2940.44
m_metamint	> 1000	2.44	0.007	0.06	2911.50
m_minimint	> 1000	2.12	0.017	0.07	2906.06
m_micromint	> 1000	2.15	0.016	0.07	2907.55
m_maia	> 1000	-2.23	0.013	0.15	2876.67
m_imaia	> 1000	1.20	0.116	0.06	2910.64
m_ias	43.15	3.99	< .001	0.02	2932.91
m_bpq	0.043	4.38	< .001	0.00	2946.74

Each model is compared to m_mint.

Biofeedback Usage (Wearables)

Number of Wearables

preds_wearN <- compare_predictors("Wearables_Number", family = "poisson")
display(preds_wearN$table, title="Wearables Number")

Wearables Number

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_SexS				0.02	2969.56	0.08
MAIA_SelfRegulation	1.06	-0.01	0.496	0.02	2969.43	0.07
BPQ	0.003	1.16	0.123	0.00	2980.92	-0.01
IAS	0.005	1.17	0.122	0.00	2979.99	0.00

Each model is compared to MINT_SexS.

preds_wearN$p

Heart

df$Wearables_HeartOwn <- ifelse(df$Wearables_Heart == "Not owning", 0, 1) 
preds_wearHeartOwn <- compare_predictors("Wearables_HeartOwn")
display(preds_wearHeartOwn$table, title="Wearables Heart Ownership")

Wearables Heart Ownership

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Resp				0.01	957.73	0.24
MAIA_SelfRegulation	15.00	-0.60	0.274	0.02	952.31	0.31
BPQ	0.012	1.19	0.117	0.00	966.60	-0.08
IAS	0.026	1.15	0.126	0.00	964.99	0.13

Each model is compared to MINT_Resp.

preds_wearHeartOwn$p

mods_wearHeartOwn <- compare_models_glm("Wearables_HeartOwn")
display(mods_wearHeartOwn$table, title="Wearables Heart Ownership")

Wearables Heart Ownership

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.03	1012.46
m_metamint	> 1000	1.70	0.045	0.01	971.82
m_minimint	> 1000	1.71	0.043	0.01	971.79
m_micromint	> 1000	1.26	0.104	0.02	966.04
m_maia	> 1000	0.09	0.464	0.03	993.65
m_imaia	> 1000	1.42	0.078	0.01	972.21
m_ias	> 1000	2.09	0.018	0.00	964.99
m_bpq	> 1000	1.99	0.023	0.00	966.60

Each model is compared to m_mint.

mods_wearHeartOwn$p

preds_wearHeartImp <- compare_predictors("Wearables_HeartImportance")
display(preds_wearHeartImp$table, title="Wearables Heart Importance")

Wearables Heart Importance

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Card				0.10	1013.08	0.37
MAIA_BodyListening	115.97	-0.64	0.262	0.13	1003.57	0.52
BPQ	< 0.001	2.52	0.006	0.01	1036.81	0.20
IAS	< 0.001	1.36	0.086	0.04	1027.99	0.04

Each model is compared to MINT_Card.

preds_wearHeartImp$p

mods_wearHeartImp <- compare_models_lm("Wearables_HeartImportance")
display(mods_wearHeartImp, title="Wearables Heart Importance")

Wearables Heart Importance

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.19	1041.78
m_metamint	> 1000	2.00	0.023	0.12	1017.22
m_minimint	> 1000	2.23	0.013	0.10	1022.77
m_micromint	10.58	2.90	0.002	0.05	1037.06
m_maia	889.20	0.19	0.423	0.18	1028.20
m_imaia	> 1000	0.80	0.212	0.15	1009.32
m_ias	987.72	3.14	< .001	0.04	1027.99
m_bpq	11.98	3.49	< .001	0.01	1036.81

Each model is compared to m_mint.

Sleep

df$Wearables_SleepOwn <- ifelse(df$Wearables_Sleep == "Not owning", 0, 1)
preds_wearSleepOwn <- compare_predictors("Wearables_SleepOwn")
display(preds_wearSleepOwn$table, title="Wearables Sleep Ownership")

Wearables Sleep Ownership

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Olfa				0.00	806.35	0.15
MAIA_SelfRegulation	1.99	-0.26	0.398	0.01	804.98	0.18
BPQ	0.235	0.84	0.201	0.00	809.25	0.01
IAS	0.269	0.73	0.232	0.00	808.98	0.05

Each model is compared to MINT_Olfa.

preds_wearSleepOwn$p

mods_wearSleepOwn <- compare_models_glm("Wearables_SleepOwn")
display(mods_wearSleepOwn$table, title="Wearables Sleep Ownership")

Wearables Sleep Ownership

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.01	865.18
m_metamint	> 1000	1.48	0.069	0.00	821.27
m_minimint	> 1000	1.52	0.065	0.00	821.72
m_micromint	> 1000	1.44	0.074	0.00	821.21
m_maia	> 1000	0.06	0.478	0.01	845.88
m_imaia	> 1000	0.95	0.170	0.00	819.45
m_ias	> 1000	1.53	0.063	0.00	808.98
m_bpq	> 1000	1.56	0.059	0.00	809.25

Each model is compared to m_mint.

preds_wearSleepImp <- compare_predictors("Wearables_SleepImportance")
display(preds_wearSleepImp$table, title="Wearables Sleep Importance")

Wearables Sleep Importance

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_RelA				0.05	707.28	0.33
MAIA_NotWorrying	0.932	0.02	0.493	0.05	707.42	-0.35
BPQ	0.121	0.56	0.287	0.02	711.51	0.33
IAS	0.038	1.10	0.135	0.01	713.81	0.02

Each model is compared to MINT_RelA.

preds_wearSleepImp$p

mods_wearSleepImp <- compare_models_lm("Wearables_SleepImportance")
display(mods_wearSleepImp, title="Wearables Sleep Importance")

Wearables Sleep Importance

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.11	747.22
m_metamint	> 1000	1.37	0.086	0.07	714.19
m_minimint	> 1000	1.67	0.047	0.05	717.03
m_micromint	> 1000	2.01	0.022	0.03	720.99
m_maia	> 1000	0.09	0.463	0.11	732.76
m_imaia	> 1000	1.42	0.077	0.05	716.83
m_ias	> 1000	1.99	0.023	0.01	713.81
m_bpq	> 1000	1.75	0.040	0.02	711.51

Each model is compared to m_mint.

Steps

df$Wearables_StepsOwn <- ifelse(df$Wearables_Steps == "Not owning", 0, 1)
preds_wearStepsOwn <- compare_predictors("Wearables_StepsOwn")
display(preds_wearStepsOwn$table, title="Wearables Steps Ownership")

Wearables Steps Ownership

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_ExaC				0.01	1030.45	-0.16
MAIA_Noticing	0.406	0.38	0.351	0.00	1032.25	-0.12
BPQ	0.257	0.58	0.282	0.00	1033.16	-0.10
IAS	0.222	0.86	0.195	0.00	1033.45	-0.09

Each model is compared to MINT_ExaC.

preds_wearStepsOwn$p

mods_wearStepsOwn <- compare_models_glm("Wearables_StepsOwn")
display(mods_wearStepsOwn$table, title="Wearables Steps Ownership")

Wearables Steps Ownership

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.01	1090.82
m_metamint	> 1000	1.42	0.078	0.00	1046.01
m_minimint	> 1000	1.16	0.123	0.01	1043.38
m_micromint	> 1000	1.16	0.123	0.01	1043.39
m_maia	> 1000	0.29	0.385	0.01	1073.43
m_imaia	> 1000	1.00	0.159	0.00	1045.00
m_ias	> 1000	1.42	0.078	0.00	1033.45
m_bpq	> 1000	1.24	0.108	0.00	1033.16

Each model is compared to m_mint.

preds_wearStepsImp <- compare_predictors("Wearables_StepsImportance")
display(preds_wearStepsImp$table, title="Wearables Steps Importance")

Wearables Steps Importance

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Awareness				0.07	1477.11	0.60
MAIA_Noticing	5.68	-0.26	0.399	0.08	1473.63	0.50
BPQ	< 0.001	1.63	0.052	0.02	1495.95	0.26
IAS	0.017	0.81	0.209	0.05	1485.31	0.04

Each model is compared to MINT_Awareness.

preds_wearStepsImp$p

mods_wearStepsImp <- compare_models_lm("Wearables_StepsImportance")
display(mods_wearStepsImp, title="Wearables Steps Importance")

Wearables Steps Importance

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.15	1501.87
m_metamint	> 1000	2.28	0.011	0.10	1476.89
m_minimint	> 1000	2.49	0.006	0.09	1481.29
m_micromint	521.34	2.85	0.002	0.07	1489.35
m_maia	0.601	1.33	0.091	0.11	1502.88
m_imaia	> 1000	1.68	0.047	0.09	1478.59
m_ias	> 1000	3.07	0.001	0.05	1485.31
m_bpq	19.31	3.71	< .001	0.02	1495.95

Each model is compared to m_mint.

Weight

df$Wearables_WeightOwn <- ifelse(df$Wearables_Weight == "Not owning", 0, 1)
preds_wearWeightOwn <- compare_predictors("Wearables_WeightOwn")
display(preds_wearWeightOwn$table, title="Wearables Weight Ownership")

Wearables Weight Ownership

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_SexS				0.02	988.73	0.27
MAIA_NotWorrying	0.011	1.18	0.120	0.00	997.67	0.12
BPQ	0.003	1.68	0.047	0.00	1000.20	0.03
IAS	0.003	1.71	0.044	0.00	1000.26	0.02

Each model is compared to MINT_SexS.

preds_wearWeightOwn$p

mods_wearWeightOwn <- compare_models_glm("Wearables_WeightOwn")
display(mods_wearWeightOwn$table, title="Wearables Weight Ownership")

Wearables Weight Ownership

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.04	1034.90
m_metamint	> 1000	2.66	0.004	0.00	1010.43
m_minimint	> 1000	2.80	0.003	0.00	1013.45
m_micromint	> 1000	2.61	0.005	0.01	1009.67
m_maia	0.178	1.87	0.031	0.01	1038.35
m_imaia	> 1000	2.40	0.008	0.01	1009.74
m_ias	> 1000	2.82	0.002	0.00	1000.26
m_bpq	> 1000	2.80	0.003	0.00	1000.20

Each model is compared to m_mint.

preds_wearWeightImp <- compare_predictors("Wearables_WeightImportance")
display(preds_wearWeightImp$table, title="Wearables Weight Importance")

Wearables Weight Importance

Name	BF	LR	p (LR)	R2	BIC	Coefficient
MINT_Awareness				0.02	1076.27	0.27
MAIA_Noticing	12.79	-0.71	0.239	0.04	1071.17	0.29
BPQ	0.066	1.09	0.138	0.00	1081.69	0.05
IAS	12.84	-0.92	0.180	0.04	1071.16	0.03

Each model is compared to MINT_Awareness.

preds_wearWeightImp$p

mods_wearWeightImp <- compare_models_lm("Wearables_WeightImportance")
display(mods_wearWeightImp, title="Wearables Weight Importance")

Wearables Weight Importance

Name	BF	LR	p (LR)	R2	BIC
m_mint				0.06	1120.93
m_metamint	> 1000	1.45	0.073	0.03	1083.56
m_minimint	> 1000	1.28	0.100	0.04	1082.22
m_micromint	> 1000	1.61	0.053	0.03	1084.71
m_maia	> 1000	-0.38	0.350	0.07	1100.16
m_imaia	> 1000	0.52	0.301	0.04	1080.23
m_ias	> 1000	0.80	0.213	0.04	1071.16
m_bpq	> 1000	2.16	0.015	0.00	1081.69

Each model is compared to m_mint.

Summary Table

make_table1 <- function(preds=preds_dif, mods=mods_dif, group = "Alexithymia", outcome = "TAS - DIF") {
  yvar <- preds$outcome
  preds <- preds$table
  
  best_pred_mint <- preds[str_detect(preds$Name, "MINT"), ]
  best_pred_nonmint <- preds[!str_detect(preds$Name, "MINT"), ]
  best_pred_nonmint <- best_pred_nonmint[best_pred_nonmint$R2 == max(best_pred_nonmint$R2), ][1,]
  best_models_R2 <- str_remove(arrange(mods, desc(as.numeric(R2)))$Name, "m_")
  best_model_BIC <- str_remove(arrange(mods, as.numeric(BIC))$Name, "m_")
  
  if(length(unique(df[[yvar]])) > 2) {
    # LMs - display correlation
    r_mint <- cor.test(cbind(df, metamint)[[best_pred_mint$Name[1]]], df[[yvar]])
    r_mint <- paste0(insight::format_value(r_mint$estimate, zap_small = TRUE))
    r_nonmint <- cor.test(df[[best_pred_nonmint$Name[1]]], df[[yvar]])
    r_nonmint <- paste0(insight::format_value(r_nonmint$estimate, zap_small = TRUE))
  } else {
    # GLMs - display log-odds
    r_mint <- best_pred_mint$Coefficient
    r_nonmint <- best_pred_nonmint$Coefficient
  }
  
  # Format
  best_models_R2[str_detect(best_models_R2, "mint")] <- paste0("<b>", best_models_R2[str_detect(best_models_R2, "mint")], "</b>")
  best_model_BIC[str_detect(best_model_BIC, "mint")] <- paste0("<b>", best_model_BIC[str_detect(best_model_BIC, "mint")], "</b>")
  best_models_R2 <- paste0("<small>", paste0(best_models_R2, collapse = " > "), "</small>")
  best_model_BIC <- paste0("<small>", paste0(best_model_BIC, collapse = " > "), "</small>")
  
  
  data.frame(
    Group = group,
    Outcome = outcome, 
    Best_MINT_Predictor = paste0(str_remove(best_pred_mint$Name[1], "MINT_"), "<br><small><i>(", r_mint, ")</i></small>"),
    Best_NonMINT_Predictor = paste0(str_replace(best_pred_nonmint$Name[1], "_", " - "), "<br><small><i>(",  r_nonmint, ")</i></small>"),
    Best_Models_R2 = best_models_R2,
    Best_Models_BIC = best_model_BIC
    )
}

rez <- rbind(
  make_table1(preds_dif, mods_dif, group = "Alexithymia", outcome = "Difficulty Identifying Feelings (DIF)<small><p align='right'><i>TAS-20</i></small><p>"),
  make_table1(preds_ddf, mods_ddf, group = "Alexithymia", outcome = "Difficulty Describing Feelings (DDF)<small><p align='right'><i>TAS-20</i></small><p>"),
  make_table1(preds_eot, mods_eot, group = "Alexithymia", outcome = "Externally Oriented Thinking (EOT)<small><p align='right'><i>TAS-20</i></small><p>"),
  
  make_table1(preds_arou, mods_arou, group = "Emotion Reactivity", outcome = "Arousal<small><p align='right'><i>ERS</i></small><p>"),
  make_table1(preds_sens, mods_sens, group = "Emotion Reactivity", outcome = "Sensitivity<small><p align='right'><i>ERS</i></small><p>"),
  make_table1(preds_pers, mods_pers, group = "Emotion Reactivity", outcome = "Persistence<small><p align='right'><i>ERS</i></small><p>"),
  
  make_table1(preds_ls, mods_ls, group = "Mood", outcome = "Life Satisfaction"),
  make_table1(preds_anx, mods_anx, group = "Mood", outcome = "Anxiety<small><p align='right'><i>PHQ-4</i></small><p>"),
  make_table1(preds_dep, mods_dep, group = "Mood", outcome = "Depression<small><p align='right'><i>PHQ-4</i></small><p>"),
  
  make_table1(preds_moodt, mods_moodt$table, group = "Mental Health", outcome = "Mood Disorder"),
  make_table1(preds_adhd, mods_adhd$table, group = "Mental Health", outcome = "ADHD"),
  make_table1(preds_asd, mods_asd$table, group = "Mental Health", outcome = "Autism"),
  
  make_table1(preds_afferent, mods_afferent$table, group = "Somatic Health", outcome = "Afferent Sensitivity<small><small><p align='right'><i>Migraine, neuropathy, muscle tension, dizziness, ...</i></small></small><p>"),
  make_table1(preds_central, mods_central$table, group = "Somatic Health", outcome = "Central Sensitization<small><small><p align='right'><i>Fibromyalgia, chronic fatigue, back pain, IBS, ...</i></small></small><p>"),
  make_table1(preds_autonomic, mods_autonomic$table, group = "Somatic Health", outcome = "Autonomic Dysfunction<small><small><p align='right'><i>Hypermobility, chest pain, hypo/hypertension, ...</i></small></small><p>"),
  make_table1(preds_immune, mods_immune$table, group = "Somatic Health", outcome = "Immune-Inflammatory<small><small><p align='right'><i>Allergies, eczema, autoimmune, ...</i></small></small><p>"),
  
  make_table1(preds_cefsabody, mods_cefsabody, group = "Dissociative Symptoms", outcome = "Body<small><p align='right'><i>CEFSA</i></small><p>"),
  make_table1(preds_cefsaself, mods_cefsaself, group = "Dissociative Symptoms", outcome = "Self<small><p align='right'><i>CEFSA</i></small><p>"),
  make_table1(preds_cefsaemo, mods_cefsaemo, group = "Dissociative Symptoms", outcome = "Emotions<small><p align='right'><i>CEFSA</i></small><p>"),
  make_table1(preds_cefsareal, mods_cefsareal, group = "Dissociative Symptoms", outcome = "Reality<small><p align='right'><i>CEFSA</i></small><p>"),
  
  # make_table1(preds_pialive, mods_pialive, group = "Primals", outcome = "Alive<small><p align='right'><i>PI-18</i></small><p>"),
  # make_table1(preds_pisafe, mods_pisafe, group = "Primals", outcome = "Safe<small><p align='right'><i>PI-18</i></small><p>"),
  # make_table1(preds_pient, mods_pient, group = "Primals", outcome = "Enticing<small><p align='right'><i>PI-18</i></small><p>"),
  # make_table1(preds_pigood, mods_pigood, group = "Primals", outcome = "Good<small><p align='right'><i>PI-18</i></small><p>")
  
  make_table1(preds_bmi, mods_bmi, group = "Lifestyle", outcome = "BMI"),
  make_table1(preds_physactiv, mods_physactiv, group = "Lifestyle", outcome = "Physical Activity"),
  # make_table1(preds_age, mods_age, group = "Lifestyle", outcome = "Age"),
  make_table1(preds_wearHeartImp, mods_wearHeartImp, group = "Lifestyle", outcome = "Cardiac Monitoring"),
  make_table1(preds_wearSleepImp, mods_wearSleepImp, group = "Lifestyle", outcome = "Sleep Monitoring"),
  make_table1(preds_wearStepsImp, mods_wearStepsImp, group = "Lifestyle", outcome = "Steps Monitoring")
  # make_table1(preds_wearWeightImp, mods_wearWeightImp, group = "Lifestyle", outcome = "Weight Monitoring")
)

tab2 <- rez |> 
  gt::gt() |> 
  gt::tab_header(
    title = gt::md("**Interoceptive Questionnaires Comparison**"),
    subtitle = "MINT vs. MAIA, IAS, BPQ"
  ) |> 
  gt::cols_width(
    Outcome ~ gt::pct(23),
    Best_MINT_Predictor ~ gt::pct(12),
    Best_NonMINT_Predictor ~ gt::pct(15),
    Best_Models_R2 ~ gt::pct(25),
    Best_Models_BIC ~ gt::pct(25)
  ) |> 
  gt::tab_style(
    style = gt::cell_text(weight="bold", v_align = "middle"),
    locations = gt::cells_column_labels()
  )  |>
  gt::tab_style(
    style = gt::cell_text(align = "center", v_align = "middle"),
    locations = gt::cells_body(columns=c("Best_MINT_Predictor", "Best_NonMINT_Predictor"))
  )  |>
  gt::tab_row_group(
    label = "Lifestyle",
    rows = Group == "Lifestyle"
  ) |> 
  gt::tab_row_group(
    label = "Dissociative Symptoms",
    rows = Group == "Dissociative Symptoms"
  ) |> 
  gt::tab_row_group(
    label = "Primal World Beliefs",
    rows = Group == "Primals"
  ) |> 
  gt::tab_row_group(
    label = "Somatic Health",
    rows = Group == "Somatic Health"
  ) |> 
  gt::tab_row_group(
    label = "Mental Health",
    rows = Group == "Mental Health"
  ) |>
  gt::tab_row_group(
    label = "Mood",
    rows = Group == "Mood"
  ) |> 
  gt::tab_row_group(
    label = "Emotional Reactivity",
    rows = Group == "Emotion Reactivity"
  ) |> 
  gt::tab_row_group(
    label = "Alexithymia",
    rows = Group == "Alexithymia"
  ) |> 
  gt::tab_style(
    style = list(
      gt::cell_fill(color = "gray85"),
      gt::cell_text(weight="bold", style="italic")),
    locations = gt::cells_row_groups()
  )  |> 
  gt::tab_style(
    style = gt::cell_fill(color = "#81C784"),
    locations = list(
      gt::cells_body(columns="Best_MINT_Predictor", rows=c(1:4, 11:18, 20:21, 24))
    )
  ) |> 
  gt::tab_style(
    style = gt::cell_fill(color = "#FFB74D"),
    locations = list(
      gt::cells_body(columns="Best_MINT_Predictor", rows=c(5:9, 10, 19, 22))
    )
  ) |> 
  gt::tab_style(
    style = gt::cell_fill(color = "#EF9A9A"),
    locations = list(
      gt::cells_body(columns="Best_MINT_Predictor", rows=c(23, 25))
    )
  ) |> 
  gt::tab_style(
    style = gt::cell_fill(color = "#81C784"),
    locations = list(
      gt::cells_body(columns="Best_Models_R2", rows=c(1:3, 11:13, 15:21, 23:25))
    )
  ) |>
  gt::tab_style(
    style = gt::cell_fill(color = "#FFB74D"),
    locations = list(
      gt::cells_body(columns="Best_Models_R2", rows=c(4:10, 14, 22))
    )
  ) |>
  gt::tab_style(
    style = gt::cell_fill(color = "#81C784"),
    locations = list(
      gt::cells_body(columns="Best_Models_BIC", rows=c(1:3, 11:20, 25))
    )
  ) |>
  gt::tab_style(
    style = gt::cell_fill(color = "#FFB74D"),
    locations = list(
      gt::cells_body(columns="Best_Models_BIC", rows=c(4:10, 22))
    )
  ) |>
  gt::tab_style(
    style = gt::cell_fill(color = "#EF9A9A"),
    locations = list(
      gt::cells_body(columns="Best_Models_BIC", rows=c(21, 23, 24))
    )
  ) |>
  gt::cols_label(
    Best_MINT_Predictor = "Best Predictor\n(MINT)",
    Best_NonMINT_Predictor = "Best Predictor\n(Non-MINT)",
    Best_Models_R2 = "Best Models (R2)",
    Best_Models_BIC = "Best Models (BIC)"
  ) |> 
  gt::cols_hide(Group) |> 
  gt::fmt_markdown() |> 
  gt::tab_options(
    table.font.size = gt::pct(125)
  )


tab2 |> 
  gt::tab_options(
    table.font.size = gt::pct(50)
  ) |> 
  gt::opt_interactive()

Interoceptive Questionnaires Comparison

MINT vs. MAIA, IAS, BPQ

gt::gtsave(tab2, "figures/table2.png", vwidth=1400, expand=0, quiet=TRUE)

Figures

fig1a <- patchwork::wrap_elements(
  patchwork::wrap_elements(grid::rasterGrob(png::readPNG("figures/fig1a.png"))) +
  patchwork::plot_annotation(title = "Exploratory Graphical Analysis (EGA)",
                             subtitle = "Bootstrapped replication of hierarchical clusters (Methhod = leiden)",
                             theme = theme(plot.title = element_text(face="bold", hjust = 0, size = rel(1.4)),
                                           plot.subtitle = element_text(hjust = 0, size = rel(1)))))
fig1b <- patchwork::wrap_elements(
  patchwork::wrap_elements(grid::rasterGrob(png::readPNG("figures/fig1b.png"))) +
  patchwork::plot_annotation(title = "Hierarchical Clustering",
                             subtitle = "Method = Correlation",
                             theme = theme(plot.title = element_text(face="bold", hjust = 0, size = rel(1.4)),
                                           plot.subtitle = element_text(hjust = 0, size = rel(1)))))
ggsave("figures/fig1.png", fig1a / fig1b, width=8, height=12, dpi=300)




p_cor <- df |> 
  select(starts_with("TAS_"), LifeSatisfaction, starts_with("PHQ4_"), starts_with("CEFSA"), starts_with("PI"), starts_with("ERS_")) |> 
  mutate(LifeSatisfaction = datawizard::reverse_scale(LifeSatisfaction),
         PI_Safe = datawizard::reverse_scale(PI_Safe),
         PI_Good = datawizard::reverse_scale(PI_Good),
         PI_Enticing = datawizard::reverse_scale(PI_Enticing)) |>
  rename(`Life Satisfaction*` = LifeSatisfaction,
         `Primals - Alive` = PI_Alive,
         `Primals - Safe*` = PI_Safe,
         `Primals - Good*` = PI_Good,
         `Primals - Enticing*` = PI_Enticing) |>
  make_cor(cbind(mint, metamint, select(df, starts_with("MAIA"), IAS, BPQ))) +
  labs(title="Correlates of Interoception") 
p_cor

fig2 <- (p_cormint | p_corintero) / p_cor + plot_layout(heights = c(0.4, 0.6))
ggsave("figures/fig2.png", fig2, width=10, height=12, dpi=300)





fig3 <- (mods_moodt$p + theme(axis.text.x = element_blank(), axis.title.x = element_blank()) | 
    mods_adhd$p + theme(axis.text.x = element_blank(), axis.title.x = element_blank()) | 
    mods_asd$p + theme(axis.text.x = element_blank(), axis.title.x = element_blank())) / 
  (mods_central$p | mods_autonomic$p | mods_immune$p) +
  plot_layout(guides = "collect") +
  plot_annotation(title = "Predictive Performance of Interoceptive Questionnaires", 
       subtitle = "ROC Curves",
       theme = theme(plot.title = element_text(face="bold", hjust = 0.5, size = rel(1.4)),
                     plot.subtitle = element_text(hjust = 0.5, size = rel(1.25))))
fig3
ggsave("figures/fig3.png", fig3, width=13, height=9, dpi=300)